From 4a8f54db02688bc2e62d2a42c6d0c975a3719ed0 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Thu, 26 Nov 2020 07:10:47 +0100 Subject: [PATCH] week 48 --- doc/pub/week48/html/._week48-bs000.html | 195 +++++++------- doc/pub/week48/html/._week48-bs001.html | 195 +++++++------- doc/pub/week48/html/._week48-bs002.html | 195 +++++++------- doc/pub/week48/html/._week48-bs003.html | 195 +++++++------- doc/pub/week48/html/._week48-bs004.html | 195 +++++++------- doc/pub/week48/html/._week48-bs005.html | 195 +++++++------- doc/pub/week48/html/._week48-bs006.html | 195 +++++++------- doc/pub/week48/html/._week48-bs007.html | 195 +++++++------- doc/pub/week48/html/._week48-bs008.html | 195 +++++++------- doc/pub/week48/html/._week48-bs009.html | 195 +++++++------- doc/pub/week48/html/._week48-bs010.html | 195 +++++++------- doc/pub/week48/html/._week48-bs011.html | 195 +++++++------- doc/pub/week48/html/._week48-bs012.html | 195 +++++++------- doc/pub/week48/html/._week48-bs013.html | 195 +++++++------- doc/pub/week48/html/._week48-bs014.html | 195 +++++++------- doc/pub/week48/html/._week48-bs015.html | 195 +++++++------- doc/pub/week48/html/._week48-bs016.html | 195 +++++++------- doc/pub/week48/html/._week48-bs017.html | 200 ++++++++------- doc/pub/week48/html/._week48-bs018.html | 198 ++++++++------- doc/pub/week48/html/._week48-bs019.html | 205 ++++++++------- doc/pub/week48/html/._week48-bs020.html | 208 +++++++-------- doc/pub/week48/html/._week48-bs021.html | 240 ++++++++---------- doc/pub/week48/html/._week48-bs022.html | 253 ++++++++++--------- doc/pub/week48/html/._week48-bs023.html | 230 +++++++++-------- doc/pub/week48/html/._week48-bs024.html | 215 ++++++++-------- doc/pub/week48/html/._week48-bs025.html | 210 +++++++-------- doc/pub/week48/html/._week48-bs026.html | 205 +++++++-------- doc/pub/week48/html/._week48-bs027.html | 221 ++++++++-------- doc/pub/week48/html/._week48-bs028.html | 216 ++++++++-------- doc/pub/week48/html/._week48-bs029.html | 219 ++++++++-------- doc/pub/week48/html/._week48-bs030.html | 219 ++++++++-------- doc/pub/week48/html/._week48-bs031.html | 210 +++++++-------- doc/pub/week48/html/._week48-bs032.html | 210 ++++++++------- doc/pub/week48/html/._week48-bs033.html | 215 ++++++++-------- doc/pub/week48/html/._week48-bs034.html | 233 +++++++++-------- doc/pub/week48/html/._week48-bs035.html | 223 ++++++++-------- doc/pub/week48/html/._week48-bs036.html | 215 ++++++++-------- doc/pub/week48/html/._week48-bs037.html | 219 ++++++++-------- doc/pub/week48/html/._week48-bs038.html | 231 ++++++++--------- doc/pub/week48/html/._week48-bs039.html | 218 ++++++++-------- doc/pub/week48/html/._week48-bs040.html | 227 ++++++++++------- doc/pub/week48/html/._week48-bs041.html | 205 +++++++-------- doc/pub/week48/html/._week48-bs042.html | 212 ++++++++-------- doc/pub/week48/html/._week48-bs043.html | 228 ++++++++--------- doc/pub/week48/html/._week48-bs044.html | 227 ++++++++--------- doc/pub/week48/html/._week48-bs045.html | 237 +++++++++-------- doc/pub/week48/html/._week48-bs046.html | 241 ++++++++++-------- doc/pub/week48/html/._week48-bs047.html | 239 ++++++++---------- doc/pub/week48/html/._week48-bs048.html | 231 +++++++++-------- doc/pub/week48/html/._week48-bs049.html | 227 ++++++++--------- doc/pub/week48/html/._week48-bs050.html | 231 +++++++++-------- doc/pub/week48/html/._week48-bs051.html | 216 ++++++++-------- doc/pub/week48/html/._week48-bs052.html | 218 ++++++++-------- doc/pub/week48/html/._week48-bs053.html | 210 +++++++-------- doc/pub/week48/html/._week48-bs054.html | 208 +++++++-------- doc/pub/week48/html/._week48-bs055.html | 221 ++++++++-------- doc/pub/week48/html/._week48-bs056.html | 228 +++++++++-------- doc/pub/week48/html/._week48-bs057.html | 230 ++++++++--------- doc/pub/week48/html/._week48-bs058.html | 232 +++++++++-------- doc/pub/week48/html/._week48-bs059.html | 215 ++++++++-------- doc/pub/week48/html/._week48-bs060.html | 215 ++++++++-------- doc/pub/week48/html/._week48-bs061.html | 219 ++++++++-------- doc/pub/week48/html/._week48-bs062.html | 212 ++++++++-------- doc/pub/week48/html/._week48-bs063.html | 204 ++++++++------- doc/pub/week48/html/week48-bs.html | 195 +++++++------- doc/pub/week48/html/week48-reveal.html | 102 ++++---- doc/pub/week48/html/week48-solarized.html | 200 ++++++++------- doc/pub/week48/html/week48.html | 200 ++++++++------- doc/pub/week48/ipynb/ipynb-week48-src.tar.gz | Bin 822634 -> 822634 bytes doc/pub/week48/ipynb/week48.ipynb | 2 + doc/src/week48/week48.do.txt | 3 + 71 files changed, 7368 insertions(+), 6995 deletions(-) diff --git a/doc/pub/week48/html/._week48-bs000.html b/doc/pub/week48/html/._week48-bs000.html index dc68b9446..a8d8e7df6 100644 --- a/doc/pub/week48/html/._week48-bs000.html +++ b/doc/pub/week48/html/._week48-bs000.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - 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  • What? Me worry? No final exam in this course!
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  • Perspective on Machine Learning
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  • Types of Machine Learning, a repetition
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  • Quantum deep learning
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  • Best wishes to you all and thanks so much for your heroic efforts this semester
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  • SVMs and Regression and multiclass classification
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  • Summary of course
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  • What? Me worry? No final exam in this course!
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  • Topics we have covered this year
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  • Statistical analysis and optimization of data
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  • Machine learning
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  • Learning outcomes and overarching aims of this course
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  • Perspective on Machine Learning
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  • Machine Learning Research
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  • Starting your Machine Learning Project
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  • Choose a Model and Algorithm
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  • Which Activation and Weights to Choose in Neural Networks
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  • Optimization Methods and Hyperparameters
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  • Resampling
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  • Other courses on Data science and Machine Learning at UiO
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  • Types of Machine Learning, a repetition
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  • Why Boltzmann machines?
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  • Boltzmann Machines
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  • Some similarities and differences from DNNs
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  • Boltzmann machines (BM)
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  • A standard BM setup
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  • The structure of the RBM network
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  • The network
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  • Goals
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  • Joint distribution
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  • Network Elements, the energy function
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  • Defining different types of RBMs
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  • More about RBMs
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  • Autoencoders: Overarching view
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  • Bayesian Machine Learning
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  • Transfer learning
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  • Dual learning
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  • Meta learning
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  • The Challenges Facing Machine Learning
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  • Quantum machine learning
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  • Quantum machine learning algorithms based on linear algebra
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  • Quantum reinforcement learning
    • +
  • Quantum deep learning
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  • Social machine learning
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  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -301,7 +306,7 @@ MathJax.Hub.Config({
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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
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  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
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  • Adversarial learning
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  • Dual learning
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  • Distributed machine learning
    • +
  • Meta learning
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  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
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  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -284,7 +289,7 @@ Geron's chapter 5. Chapter 12 (sections 12.1-12.3 are the most relevant ones) o
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    • diff --git a/doc/pub/week48/html/._week48-bs002.html b/doc/pub/week48/html/._week48-bs002.html index cd19ad1f4..962322457 100644 --- a/doc/pub/week48/html/._week48-bs002.html +++ b/doc/pub/week48/html/._week48-bs002.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -281,7 +286,7 @@ We finalize our discussion on Support Vector Machines with an emphasis on kernel
  • 11
  • 12
  • ...
    • -
  • 64
    • +
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs003.html b/doc/pub/week48/html/._week48-bs003.html index 8168b3b23..81c278d66 100644 --- a/doc/pub/week48/html/._week48-bs003.html +++ b/doc/pub/week48/html/._week48-bs003.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -284,7 +289,7 @@ modern research projects in machine learning.
  • 12
  • 13
  • ...
    • -
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    • +
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    • diff --git a/doc/pub/week48/html/._week48-bs004.html b/doc/pub/week48/html/._week48-bs004.html index 9e890d393..4ebc7ec89 100644 --- a/doc/pub/week48/html/._week48-bs004.html +++ b/doc/pub/week48/html/._week48-bs004.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -302,7 +307,7 @@ problems. I recommend you take a look at the lectures from last week on the bin
  • 13
  • 14
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs005.html b/doc/pub/week48/html/._week48-bs005.html index 30a97c438..b37bf0d41 100644 --- a/doc/pub/week48/html/._week48-bs005.html +++ b/doc/pub/week48/html/._week48-bs005.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -349,7 +354,7 @@ plt.show()
  • 14
  • 15
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs006.html b/doc/pub/week48/html/._week48-bs006.html index 703810d96..c599020e7 100644 --- a/doc/pub/week48/html/._week48-bs006.html +++ b/doc/pub/week48/html/._week48-bs006.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -323,7 +328,7 @@ kernel trick.
  • 15
  • 16
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs007.html b/doc/pub/week48/html/._week48-bs007.html index bd4e0a5f0..53618c612 100644 --- a/doc/pub/week48/html/._week48-bs007.html +++ b/doc/pub/week48/html/._week48-bs007.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
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  • Additional courses of interest
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  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -303,7 +308,7 @@ If we add the slack constants this leads to the additional constraint \( 0\leq \
  • 16
  • 17
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs008.html b/doc/pub/week48/html/._week48-bs008.html index 212170c3c..bbab9d524 100644 --- a/doc/pub/week48/html/._week48-bs008.html +++ b/doc/pub/week48/html/._week48-bs008.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -297,7 +302,7 @@ Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.
  • 17
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  • ...
    • -
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs009.html b/doc/pub/week48/html/._week48-bs009.html index b6b72dce2..0540aaacf 100644 --- a/doc/pub/week48/html/._week48-bs009.html +++ b/doc/pub/week48/html/._week48-bs009.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -318,7 +323,7 @@ in practice.
  • 18
  • 19
  • ...
    • -
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    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs010.html b/doc/pub/week48/html/._week48-bs010.html index e1b4e9138..ba59324f5 100644 --- a/doc/pub/week48/html/._week48-bs010.html +++ b/doc/pub/week48/html/._week48-bs010.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -477,7 +482,7 @@ plt.show()
  • 19
  • 20
  • ...
    • -
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    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs011.html b/doc/pub/week48/html/._week48-bs011.html index 9d1d38af1..28835354b 100644 --- a/doc/pub/week48/html/._week48-bs011.html +++ b/doc/pub/week48/html/._week48-bs011.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -306,7 +311,7 @@ Convex optimization problems play a central role in applied mathematics and we r
  • 20
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  • ...
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs012.html b/doc/pub/week48/html/._week48-bs012.html index e8fc6bc04..db848c836 100644 --- a/doc/pub/week48/html/._week48-bs012.html +++ b/doc/pub/week48/html/._week48-bs012.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -307,7 +312,7 @@ This will make our life much easier. You don't need to write your own optimizer.
  • 21
  • 22
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs013.html b/doc/pub/week48/html/._week48-bs013.html index d7c2da315..1e97c32a4 100644 --- a/doc/pub/week48/html/._week48-bs013.html +++ b/doc/pub/week48/html/._week48-bs013.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -349,7 +354,7 @@ sol['primal objective']
  • 22
  • 23
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs014.html b/doc/pub/week48/html/._week48-bs014.html index a74531df6..429bfca1f 100644 --- a/doc/pub/week48/html/._week48-bs014.html +++ b/doc/pub/week48/html/._week48-bs014.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -301,7 +306,7 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb
  • 23
  • 24
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs015.html b/doc/pub/week48/html/._week48-bs015.html index dff4b8890..598ecf1af 100644 --- a/doc/pub/week48/html/._week48-bs015.html +++ b/doc/pub/week48/html/._week48-bs015.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -303,7 +308,7 @@ $$
  • 24
  • 25
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs016.html b/doc/pub/week48/html/._week48-bs016.html index 65d6a6344..b36568daf 100644 --- a/doc/pub/week48/html/._week48-bs016.html +++ b/doc/pub/week48/html/._week48-bs016.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -324,7 +329,7 @@ And then we are ready to go.
  • 25
  • 26
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs017.html b/doc/pub/week48/html/._week48-bs017.html index 14398a4e8..0dee8b477 100644 --- a/doc/pub/week48/html/._week48-bs017.html +++ b/doc/pub/week48/html/._week48-bs017.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,7 +263,10 @@ MathJax.Hub.Config({ -

    Summary of course

    +

    SVMs and Regression and multiclass classification

    + +

    +See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.

    @@ -286,7 +294,7 @@ MathJax.Hub.Config({

  • 26
  • 27
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs018.html b/doc/pub/week48/html/._week48-bs018.html index 145557e7f..b6211daff 100644 --- a/doc/pub/week48/html/._week48-bs018.html +++ b/doc/pub/week48/html/._week48-bs018.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,8 +263,7 @@ MathJax.Hub.Config({ -

    What? Me worry? No final exam in this course!

    -



    +

    Summary of course

    @@ -287,7 +291,7 @@ MathJax.Hub.Config({

  • 27
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  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs019.html b/doc/pub/week48/html/._week48-bs019.html index 87f38a2a6..47d1d1f1a 100644 --- a/doc/pub/week48/html/._week48-bs019.html +++ b/doc/pub/week48/html/._week48-bs019.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,16 +263,10 @@ MathJax.Hub.Config({ -

    Topics we have covered this year

    +

    What? Me worry? No final exam in this course!

    +



    -The course has two central parts - -

      -
    1. Statistical analysis and optimization of data
      2. -
    2. Machine learning
      3. -
    -

    diff --git a/doc/pub/week48/html/._week48-bs020.html b/doc/pub/week48/html/._week48-bs020.html index b81880cac..2883650a3 100644 --- a/doc/pub/week48/html/._week48-bs020.html +++ b/doc/pub/week48/html/._week48-bs020.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
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  • Choose a Model and Algorithm
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  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
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  • Optimization Methods and Hyperparameters
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  • Resampling
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  • Other courses on Data science and Machine Learning at UiO
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  • Additional courses of interest
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  • What's the future like?
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  • Types of Machine Learning, a repetition
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  • Why Boltzmann machines?
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  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
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  • A standard BM setup
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  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,19 +263,14 @@ MathJax.Hub.Config({ -

    Statistical analysis and optimization of data

    +

    Topics we have covered this year

    -The following topics be covered +The course has two central parts

      -
    1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;
      2. -
    2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
      3. -
    3. Central elements from linear algebra
      4. -
    4. Gradient methods for data optimization
      5. -
    5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;
      6. -
    6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
      7. -
    7. Principal Component Analysis.
      8. +
    8. Statistical analysis and optimization of data
      9. +
    9. Machine learning

    @@ -298,7 +298,7 @@ The following topics be covered

  • 29
  • 30
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs021.html b/doc/pub/week48/html/._week48-bs021.html index 8ca67668d..f0a16b5dd 100644 --- a/doc/pub/week48/html/._week48-bs021.html +++ b/doc/pub/week48/html/._week48-bs021.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,46 +263,19 @@ MathJax.Hub.Config({ -

    Machine learning

    +

    Statistical analysis and optimization of data

    -The following topics will be covered +The following topics be covered

      -
    1. Linear methods for regression and classification: - -
        -
      1. Ordinary Least Squares
        2. -
      2. Ridge regression
        3. -
      3. Lasso regression
        4. -
      4. Logistic regression
        5. -
      - -
    2. Neural networks and deep learning: - -
        -
      1. Feed Forward Neural Networks
        2. -
      2. Convolutional Neural Networks
        3. -
      3. Recurrent Neural Networks
        4. -
      - -
    3. Decisions trees and ensemble methods: - -
        -
      1. Decision trees
        2. -
      2. Bagging and voting
        3. -
      3. Random forests
        4. -
      4. Boosting and gradient boosting
        5. -
      - -
    4. Support vector machines - -
        -
      1. Binary classification and multiclass classification
        2. -
      2. Kernel methods
        3. -
      3. Regression
        4. -
      - +
    5. Basic concepts, expectation values, variance, covariance, correlation functions and errors;
      6. +
    6. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
      7. +
    7. Central elements from linear algebra
      8. +
    8. Gradient methods for data optimization
      9. +
    9. Estimation of errors using cross-validation, bootstrapping and jackknife methods;
      10. +
    10. Practical optimization using Singular-value decomposition and least squares for parameterizing data.
      11. +
    11. Principal Component Analysis.

    @@ -325,7 +303,7 @@ The following topics will be covered

  • 30
  • 31
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs022.html b/doc/pub/week48/html/._week48-bs022.html index cba3affaf..7c1b9ac88 100644 --- a/doc/pub/week48/html/._week48-bs022.html +++ b/doc/pub/week48/html/._week48-bs022.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,29 +263,47 @@ MathJax.Hub.Config({ -

    Learning outcomes and overarching aims of this course

    +

    Machine learning

    -The course introduces a variety of central algorithms and methods -essential for studies of data analysis and machine learning. The -course is project based and through the various projects, normally -three, you will be exposed to fundamental research problems -in these fields, with the aim to reproduce state of the art scientific -results. The students will learn to develop and structure large codes -for studying these systems, get acquainted with computing facilities -and learn to handle large scientific projects. A good scientific and -ethical conduct is emphasized throughout the course. +The following topics will be covered -

    +
      +
    1. Linear methods for regression and classification: + +
        +
      1. Ordinary Least Squares
        2. +
      2. Ridge regression
        3. +
      3. Lasso regression
        4. +
      4. Logistic regression
        5. +
      + +
    2. Neural networks and deep learning: + +
        +
      1. Feed Forward Neural Networks
        2. +
      2. Convolutional Neural Networks
        3. +
      3. Recurrent Neural Networks
        4. +
      + +
    3. Decisions trees and ensemble methods: + +
        +
      1. Decision trees
        2. +
      2. Bagging and voting
        3. +
      3. Random forests
        4. +
      4. Boosting and gradient boosting
        5. +
      + +
    4. Support vector machines + +
        +
      1. Binary classification and multiclass classification
        2. +
      2. Kernel methods
        3. +
      3. Regression
        4. +
      + +

    @@ -307,7 +330,7 @@ ethical conduct is emphasized throughout the course.

  • 31
  • 32
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs023.html b/doc/pub/week48/html/._week48-bs023.html index c2fe0e5aa..0d6067894 100644 --- a/doc/pub/week48/html/._week48-bs023.html +++ b/doc/pub/week48/html/._week48-bs023.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,21 +263,30 @@ MathJax.Hub.Config({ -

    Perspective on Machine Learning

    - -
      -
    1. Rapidly emerging application area
      2. -
    2. Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
      3. -
    3. Requires education/retraining for more widespread adoption
      4. -
    4. A lot of “word-of-mouth” development methods
      5. -
    - -Huge amounts of data sets require automation, classical analysis tools often inadequate. -High energy physics hit this wall in the 90’s. -In 2009 single top quark production was determined via Boosted decision trees, Bayesian -Neural Networks, etc. +

    Learning outcomes and overarching aims of this course

    +The course introduces a variety of central algorithms and methods +essential for studies of data analysis and machine learning. The +course is project based and through the various projects, normally +three, you will be exposed to fundamental research problems +in these fields, with the aim to reproduce state of the art scientific +results. The students will learn to develop and structure large codes +for studying these systems, get acquainted with computing facilities +and learn to handle large scientific projects. A good scientific and +ethical conduct is emphasized throughout the course. + +

    +

    diff --git a/doc/pub/week48/html/._week48-bs024.html b/doc/pub/week48/html/._week48-bs024.html index 48ba59fa2..150c9616a 100644 --- a/doc/pub/week48/html/._week48-bs024.html +++ b/doc/pub/week48/html/._week48-bs024.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
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  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
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  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
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  • A standard BM setup
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  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,19 +263,21 @@ MathJax.Hub.Config({ -

    Machine Learning Research

    - -

    -Where to find recent results: +

    Perspective on Machine Learning

      -
    1. Conference proceedings, arXiv and blog posts!
      2. -
    2. NIPS: Neural Information Processing Systems
      3. -
    3. ICLR: International Conference on Learning Representations
      4. -
    4. ICML: International Conference on Machine Learning
      5. -
    5. Journal of Machine Learning Research
      6. +
    6. Rapidly emerging application area
      7. +
    7. Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.
      8. +
    8. Requires education/retraining for more widespread adoption
      9. +
    9. A lot of “word-of-mouth” development methods
    +Huge amounts of data sets require automation, classical analysis tools often inadequate. +High energy physics hit this wall in the 90’s. +In 2009 single top quark production was determined via Boosted decision trees, Bayesian +Neural Networks, etc. + +

    diff --git a/doc/pub/week48/html/._week48-bs025.html b/doc/pub/week48/html/._week48-bs025.html index f9ddca4f1..038379346 100644 --- a/doc/pub/week48/html/._week48-bs025.html +++ b/doc/pub/week48/html/._week48-bs025.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,14 +263,17 @@ MathJax.Hub.Config({ -

    Starting your Machine Learning Project

    +

    Machine Learning Research

    + +

    +Where to find recent results:

      -
    1. Identify problem type: classification, generation, regression
      2. -
    2. Consider your data carefully
      3. -
    3. Choose a simple model that fits 1. and 2.
      4. -
    4. Consider your data carefully again… data representation
      5. -
    5. Based on results, feedback loop to earliest possible point
      6. +
    6. Conference proceedings, arXiv and blog posts!
      7. +
    7. NIPS: Neural Information Processing Systems
      8. +
    8. ICLR: International Conference on Learning Representations
      9. +
    9. ICML: International Conference on Machine Learning
      10. +
    10. Journal of Machine Learning Research

    @@ -293,7 +301,7 @@ MathJax.Hub.Config({

  • 34
  • 35
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs026.html b/doc/pub/week48/html/._week48-bs026.html index 3e0c9a93b..946a5d67e 100644 --- a/doc/pub/week48/html/._week48-bs026.html +++ b/doc/pub/week48/html/._week48-bs026.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,12 +263,14 @@ MathJax.Hub.Config({ -

    Choose a Model and Algorithm

    +

    Starting your Machine Learning Project

      -
    1. Supervised?
      2. -
    2. Start with the simplest model that fits your problem
      3. -
    3. Start with minimal processing of data
      4. +
    4. Identify problem type: classification, generation, regression
      5. +
    5. Consider your data carefully
      6. +
    6. Choose a simple model that fits 1. and 2.
      7. +
    7. Consider your data carefully again… data representation
      8. +
    8. Based on results, feedback loop to earliest possible point

    @@ -291,7 +298,7 @@ MathJax.Hub.Config({

  • 35
  • 36
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs027.html b/doc/pub/week48/html/._week48-bs027.html index 1f1b86b9d..4ed730da5 100644 --- a/doc/pub/week48/html/._week48-bs027.html +++ b/doc/pub/week48/html/._week48-bs027.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,30 +263,12 @@ MathJax.Hub.Config({ -

    Preparing Your Data

    +

    Choose a Model and Algorithm

      -
    1. Shuffle your data
      2. -
    2. Mean center your data
      3. - - - -
    3. Normalize the variance
      4. - - - -
    4. Whitening
      5. - - - -
    5. When to do train/test split?
      6. +
    6. Supervised?
      7. +
    7. Start with the simplest model that fits your problem
      8. +
    8. Start with minimal processing of data

    @@ -309,7 +296,7 @@ MathJax.Hub.Config({

  • 36
  • 37
  • ...
    • -
  • 64
    • +
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs028.html b/doc/pub/week48/html/._week48-bs028.html index 6971f58e6..0bef89960 100644 --- a/doc/pub/week48/html/._week48-bs028.html +++ b/doc/pub/week48/html/._week48-bs028.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,23 +263,30 @@ MathJax.Hub.Config({ -

    Which Activation and Weights to Choose in Neural Networks

    +

    Preparing Your Data

      -
    1. RELU? ELU?
      2. -
    2. Sigmoid or Tanh?
      3. -
    3. Set all weights to 0?
      4. +
    4. Shuffle your data
      5. +
    5. Mean center your data
      6. -
    6. Set all weights to random values?
      7. +
    7. Normalize the variance
      8. +
    8. Whitening
      9. + + + +
    9. When to do train/test split?

    @@ -302,7 +314,7 @@ MathJax.Hub.Config({

  • 37
  • 38
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs029.html b/doc/pub/week48/html/._week48-bs029.html index 72636ddc5..c66d9ad97 100644 --- a/doc/pub/week48/html/._week48-bs029.html +++ b/doc/pub/week48/html/._week48-bs029.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,27 +263,25 @@ MathJax.Hub.Config({ -

    Optimization Methods and Hyperparameters

    +

    Which Activation and Weights to Choose in Neural Networks

      -
    1. Stochastic gradient descent - -
        -
      1. Stochastic gradient descent + momentum
        2. -
      - -
    2. State-of-the-art approaches:
      3. +
    3. RELU? ELU?
      4. +
    4. Sigmoid or Tanh?
      5. +
    5. Set all weights to 0?
      6. + +
    6. Set all weights to random values?
      7. + +
    -Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifiers, depths of trees and many other. Need to explore a large set of hyperparameters and regularization methods. - -

    diff --git a/doc/pub/week48/html/._week48-bs030.html b/doc/pub/week48/html/._week48-bs030.html index 858b10775..4df10d6e1 100644 --- a/doc/pub/week48/html/._week48-bs030.html +++ b/doc/pub/week48/html/._week48-bs030.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,17 +263,27 @@ MathJax.Hub.Config({ -

    Resampling

    - -

    -When do we resample? +

    Optimization Methods and Hyperparameters

      -
    1. Bootstrap
      2. -
    2. Cross-validation
      3. -
    3. Jackknife and many other
      4. +
    4. Stochastic gradient descent + +
        +
      1. Stochastic gradient descent + momentum
      +
    5. State-of-the-art approaches:
      6. + + + +
    + +Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifiers, depths of trees and many other. Need to explore a large set of hyperparameters and regularization methods. + +

    diff --git a/doc/pub/week48/html/._week48-bs031.html b/doc/pub/week48/html/._week48-bs031.html index 6c7b4960d..668cff0c6 100644 --- a/doc/pub/week48/html/._week48-bs031.html +++ b/doc/pub/week48/html/._week48-bs031.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,20 +263,15 @@ MathJax.Hub.Config({ -

    Other courses on Data science and Machine Learning at UiO

    +

    Resampling

    -The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. +When do we resample?

      -
    1. STK2100 Machine learning and statistical methods for prediction and classification.
      2. -
    2. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      3. -
    3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
      4. -
    4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
      5. -
    5. STK-IN4300 – Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
      6. -
    6. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
      7. -
    7. IN5400/INF5860 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
      8. -
    8. TEK5040 – Dyp læring for autonome systemer. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
      9. +
    9. Bootstrap
      10. +
    10. Cross-validation
      11. +
    11. Jackknife and many other

    @@ -299,7 +299,7 @@ The link here 40

  • 41
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs032.html b/doc/pub/week48/html/._week48-bs032.html index dd61f7971..19330bd4a 100644 --- a/doc/pub/week48/html/._week48-bs032.html +++ b/doc/pub/week48/html/._week48-bs032.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,11 +263,20 @@ MathJax.Hub.Config({ -

    Additional courses of interest

    +

    Other courses on Data science and Machine Learning at UiO

    + +

    +The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.

      -
    1. STK4051 Computational Statistics
      2. -
    2. STK4021 Applied Bayesian Analysis and Numerical Methods
      3. +
    3. STK2100 Machine learning and statistical methods for prediction and classification.
      4. +
    4. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      5. +
    5. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
      6. +
    6. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
      7. +
    7. STK-IN4300 – Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
      8. +
    8. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
      9. +
    9. IN5400/INF5860 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
      10. +
    10. TEK5040 – Dyp læring for autonome systemer. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.

    @@ -290,7 +304,7 @@ MathJax.Hub.Config({

  • 41
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  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs033.html b/doc/pub/week48/html/._week48-bs033.html index 4588ef13d..987e1f5a3 100644 --- a/doc/pub/week48/html/._week48-bs033.html +++ b/doc/pub/week48/html/._week48-bs033.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,25 +263,11 @@ MathJax.Hub.Config({ -

    What's the future like?

    - -

    -Based on multi-layer nonlinear neural networks, deep learning can -learn directly from raw data, automatically extract and abstract -features from layer to layer, and then achieve the goal of regression, -classification, or ranking. Deep learning has made breakthroughs in -computer vision, speech processing and natural language, and reached -or even surpassed human level. The success of deep learning is mainly -due to the three factors: big data, big model, and big computing. - -

    -In the past few decades, many different architectures of deep neural -networks have been proposed, such as +

    Additional courses of interest

      -
    1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
      2. -
    2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
      3. -
    3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
      4. +
    4. STK4051 Computational Statistics
      5. +
    5. STK4021 Applied Bayesian Analysis and Numerical Methods

    @@ -304,7 +295,7 @@ networks have been proposed, such as

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs034.html b/doc/pub/week48/html/._week48-bs034.html index 1ef738819..ea028807d 100644 --- a/doc/pub/week48/html/._week48-bs034.html +++ b/doc/pub/week48/html/._week48-bs034.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,35 +263,27 @@ MathJax.Hub.Config({ -

    Types of Machine Learning, a repetition

    +

    What's the future like?

    -

    -
    -

    -The approaches to machine learning are many, but are often split into two main categories. -In supervised learning we know the answer to a problem, -and let the computer deduce the logic behind it. On the other hand, unsupervised learning -is a method for finding patterns and relationship in data sets without any prior knowledge of the system. -Some authours also operate with a third category, namely reinforcement learning. This is a paradigm -of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, -solely from rewards and punishment. +Based on multi-layer nonlinear neural networks, deep learning can +learn directly from raw data, automatically extract and abstract +features from layer to layer, and then achieve the goal of regression, +classification, or ranking. Deep learning has made breakthroughs in +computer vision, speech processing and natural language, and reached +or even surpassed human level. The success of deep learning is mainly +due to the three factors: big data, big model, and big computing.

    -Another way to categorize machine learning tasks is to consider the desired output of a system. -Some of the most common tasks are: +In the past few decades, many different architectures of deep neural +networks have been proposed, such as -

      -
    • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
      • -
    • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
      • -
    • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
      • -
    • Other unsupervised learning algortihms like Boltzmann machines
      • -
    -
    -
    +
      +
    1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
      2. +
    2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
      3. +
    3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
      4. +
    - -

    diff --git a/doc/pub/week48/html/._week48-bs035.html b/doc/pub/week48/html/._week48-bs035.html index d9ebdf11d..5864ed1ad 100644 --- a/doc/pub/week48/html/._week48-bs035.html +++ b/doc/pub/week48/html/._week48-bs035.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,17 +263,33 @@ MathJax.Hub.Config({ -

    Why Boltzmann machines?

    +

    Types of Machine Learning, a repetition

    -What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. -One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. +

    +
    +

    +The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment.

    -The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. +Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are: + +

      +
    • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
      • +
    • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
      • +
    • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
      • +
    • Other unsupervised learning algortihms like Boltzmann machines
      • +
    +
    +
    -

    -Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models.

    @@ -296,7 +317,7 @@ Furthermore, they have been used to solve complicated 44

  • 45
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs036.html b/doc/pub/week48/html/._week48-bs036.html index 25b5111ae..3594423f2 100644 --- a/doc/pub/week48/html/._week48-bs036.html +++ b/doc/pub/week48/html/._week48-bs036.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,23 +263,19 @@ MathJax.Hub.Config({ -

    Boltzmann Machines

    +

    Why Boltzmann machines?

    -Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. -

    +

    +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. +

    diff --git a/doc/pub/week48/html/._week48-bs037.html b/doc/pub/week48/html/._week48-bs037.html index 7e8408a48..4e090ee6d 100644 --- a/doc/pub/week48/html/._week48-bs037.html +++ b/doc/pub/week48/html/._week48-bs037.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,17 +263,23 @@ MathJax.Hub.Config({ -

    Some similarities and differences from DNNs

    - -
      -
    1. Both use gradient-descent based learning procedures for minimizing cost functions
      2. -
    2. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.
      3. -
    3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.
      4. -
    - -History: The RBM was developed by amongst others Geoffrey Hinton, called by some the "Godfather of Deep Learning", working with the University of Toronto and Google. +

    Boltzmann Machines

    +Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? + +

    +

    diff --git a/doc/pub/week48/html/._week48-bs038.html b/doc/pub/week48/html/._week48-bs038.html index d6f6f39aa..21281a005 100644 --- a/doc/pub/week48/html/._week48-bs038.html +++ b/doc/pub/week48/html/._week48-bs038.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
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  • What? Me worry? No final exam in this course!
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  • Which Activation and Weights to Choose in Neural Networks
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  • Other courses on Data science and Machine Learning at UiO
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  • Types of Machine Learning, a repetition
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  • The structure of the RBM network
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  • The network
    • -
  • Goals
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  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
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  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
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  • Dual learning
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  • Quantum machine learning
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  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,37 +263,15 @@ MathJax.Hub.Config({ -

    Boltzmann machines (BM)

    +

    Some similarities and differences from DNNs

    -

    -

    -
    -

    -A BM is what we would call an undirected probabilistic graphical model -with stochastic continuous or discrete units. -

    -
    - -
    -
    -

    -It is interpreted as a stochastic recurrent neural network where the -state of each unit(neurons/nodes) depends on the units it is connected -to. The weights in the network represent thus the strength of the -interaction between various units/nodes. -

    -
    - -
    -
    -

    -It turns into a Hopfield network if we choose deterministic rather -than stochastic units. In contrast to a Hopfield network, a BM is a -so-called generative model. It allows us to generate new samples from -the learned distribution. -

    -
    +
      +
    1. Both use gradient-descent based learning procedures for minimizing cost functions
      2. +
    2. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.
      3. +
    3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.
      4. +
    +History: The RBM was developed by amongst others Geoffrey Hinton, called by some the "Godfather of Deep Learning", working with the University of Toronto and Google.

    @@ -316,7 +299,7 @@ the learned distribution.

  • 47
  • 48
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs039.html b/doc/pub/week48/html/._week48-bs039.html index 881a65fc4..c598c732a 100644 --- a/doc/pub/week48/html/._week48-bs039.html +++ b/doc/pub/week48/html/._week48-bs039.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,39 +263,38 @@ MathJax.Hub.Config({ -

    A standard BM setup

    +

    Boltzmann machines (BM)

    -A standard BM network is divided into a set of observable and visible units \( \hat{x} \) and a set of unknown hidden units/nodes \( \hat{h} \). +A BM is what we would call an undirected probabilistic graphical model +with stochastic continuous or discrete units.

    - -

    -Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to \( 1 \). +It is interpreted as a stochastic recurrent neural network where the +state of each unit(neurons/nodes) depends on the units it is connected +to. The weights in the network represent thus the strength of the +interaction between various units/nodes.

    - -

    -BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning +It turns into a Hopfield network if we choose deterministic rather +than stochastic units. In contrast to a Hopfield network, a BM is a +so-called generative model. It allows us to generate new samples from +the learned distribution.

    -

    -However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS. -Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below. -

    @@ -317,7 +321,7 @@ Here we take away all lateral connections between nodes in the visible layer as

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs040.html b/doc/pub/week48/html/._week48-bs040.html index 8c2d1c2b7..602e6d4a8 100644 --- a/doc/pub/week48/html/._week48-bs040.html +++ b/doc/pub/week48/html/._week48-bs040.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,10 +263,38 @@ MathJax.Hub.Config({ -

    The structure of the RBM network

    +

    A standard BM setup

    -



    +
    +
    +

    +A standard BM network is divided into a set of observable and visible units \( \hat{x} \) and a set of unknown hidden units/nodes \( \hat{h} \). +

    +
    + + +

    +

    +
    +

    +Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to \( 1 \). +

    +
    + + +

    +

    +
    +

    +BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning +

    +
    + + +

    +However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS. +Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below.

    @@ -289,7 +322,7 @@ MathJax.Hub.Config({

  • 49
  • 50
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs041.html b/doc/pub/week48/html/._week48-bs041.html index cc1b2be9f..34aeb4728 100644 --- a/doc/pub/week48/html/._week48-bs041.html +++ b/doc/pub/week48/html/._week48-bs041.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,16 +263,12 @@ MathJax.Hub.Config({ -

    The network

    +

    The structure of the RBM network

    -The network layers: - -

      -
    1. A function \( \mathbf{x} \) that represents the visible layer, a vector of \( M \) elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.
      2. -
    2. The function \( \mathbf{h} \) represents the hidden, or latent, layer. A vector of \( N \) elements (nodes). Also called "feature detectors".
      3. -
    +



    +

    diff --git a/doc/pub/week48/html/._week48-bs042.html b/doc/pub/week48/html/._week48-bs042.html index f2e0d37cf..9369de6f6 100644 --- a/doc/pub/week48/html/._week48-bs042.html +++ b/doc/pub/week48/html/._week48-bs042.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,23 +263,14 @@ MathJax.Hub.Config({ -

    Goals

    +

    The network

    -The goal of the hidden layer is to increase the model's expressive -power. We encode complex interactions between visible variables by -introducing additional, hidden variables that interact with visible -degrees of freedom in a simple manner, yet still reproduce the complex -correlations between visible degrees in the data once marginalized -over (integrated out). - -

    -The network parameters, to be optimized/learned: +The network layers:

      -
    1. \( \mathbf{a} \) represents the visible bias, a vector of same length as \( \mathbf{x} \).
      2. -
    2. \( \mathbf{b} \) represents the hidden bias, a vector of same lenght as \( \mathbf{h} \).
      3. -
    3. \( W \) represents the interaction weights, a matrix of size \( M\times N \).
      4. +
    4. A function \( \mathbf{x} \) that represents the visible layer, a vector of \( M \) elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.
      5. +
    5. The function \( \mathbf{h} \) represents the hidden, or latent, layer. A vector of \( N \) elements (nodes). Also called "feature detectors".

    @@ -302,7 +298,7 @@ over (integrated out).

  • 51
  • 52
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs043.html b/doc/pub/week48/html/._week48-bs043.html index 85f216937..361d99500 100644 --- a/doc/pub/week48/html/._week48-bs043.html +++ b/doc/pub/week48/html/._week48-bs043.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,28 +263,25 @@ MathJax.Hub.Config({ -

    Joint distribution

    +

    Goals

    -The restricted Boltzmann machine is described by a Bolztmann distribution -$$ -\begin{align} - P_{rbm}(\mathbf{x},\mathbf{h}) = \frac{1}{Z} e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})}, -\tag{1} -\end{align} -$$ - -where \( Z \) is the normalization constant or partition function, defined as -$$ -\begin{align} - Z = \int \int e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})} d\mathbf{x} d\mathbf{h}. -\tag{2} -\end{align} -$$ - -It is common to ignore \( T_0 \) by setting it to one. +The goal of the hidden layer is to increase the model's expressive +power. We encode complex interactions between visible variables by +introducing additional, hidden variables that interact with visible +degrees of freedom in a simple manner, yet still reproduce the complex +correlations between visible degrees in the data once marginalized +over (integrated out).

    +The network parameters, to be optimized/learned: + +

      +
    1. \( \mathbf{a} \) represents the visible bias, a vector of same length as \( \mathbf{x} \).
      2. +
    2. \( \mathbf{b} \) represents the hidden bias, a vector of same lenght as \( \mathbf{h} \).
      3. +
    3. \( W \) represents the interaction weights, a matrix of size \( M\times N \).
      4. +
    +

    diff --git a/doc/pub/week48/html/._week48-bs044.html b/doc/pub/week48/html/._week48-bs044.html index c975ff091..3dfc558e3 100644 --- a/doc/pub/week48/html/._week48-bs044.html +++ b/doc/pub/week48/html/._week48-bs044.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,30 +263,26 @@ MathJax.Hub.Config({ -

    Network Elements, the energy function

    +

    Joint distribution

    -The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a -configuration (pair of vectors) \( (\mathbf{x}, \mathbf{h}) \). The lower -the energy of a configuration, the higher the probability of it. This -function also depends on the parameters \( \mathbf{a} \), \( \mathbf{b} \) and -\( W \). Thus, when we adjust them during the learning procedure, we are -adjusting the energy function to best fit our problem. - -

    -An expression for the energy function is +The restricted Boltzmann machine is described by a Bolztmann distribution $$ -E(\hat{x},\hat{h}) = -\sum_{ia}^{NA}b_i^a \alpha_i^a(x_i)-\sum_{jd}^{MD}c_j^d \beta_j^d(h_j)-\sum_{ijad}^{NAMD}b_i^a \alpha_i^a(x_i)c_j^d \beta_j^d(h_j)w_{ij}^{ad}. +\begin{align} + P_{rbm}(\mathbf{x},\mathbf{h}) = \frac{1}{Z} e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})}, +\tag{1} +\end{align} $$ -

    -Here \( \beta_j^d(h_j) \) and \( \alpha_i^a(x_j) \) are so-called transfer functions that map a given input value to a desired feature value. The labels \( a \) and \( d \) denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. Note that there is no connection between nodes in a layer. +where \( Z \) is the normalization constant or partition function, defined as +$$ +\begin{align} + Z = \int \int e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})} d\mathbf{x} d\mathbf{h}. +\tag{2} +\end{align} +$$ -

    -The quantities \( b \) and \( c \) can be interpreted as the visible and hidden biases, respectively. - -

    -The connection between the nodes in the two layers is given by the weights \( w_{ij} \). +It is common to ignore \( T_0 \) by setting it to one.

    @@ -309,7 +310,7 @@ The connection between the nodes in the two layers is given by the weights \( w_

  • 53
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    • diff --git a/doc/pub/week48/html/._week48-bs045.html b/doc/pub/week48/html/._week48-bs045.html index 6ebbd1fd0..316737ec3 100644 --- a/doc/pub/week48/html/._week48-bs045.html +++ b/doc/pub/week48/html/._week48-bs045.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,42 +263,30 @@ MathJax.Hub.Config({ -

    Defining different types of RBMs

    -There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \). +

    Network Elements, the energy function

    -

    -
    -

    +The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a +configuration (pair of vectors) \( (\mathbf{x}, \mathbf{h}) \). The lower +the energy of a configuration, the higher the probability of it. This +function also depends on the parameters \( \mathbf{a} \), \( \mathbf{b} \) and +\( W \). Thus, when we adjust them during the learning procedure, we are +adjusting the energy function to best fit our problem.

    -RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows: +An expression for the energy function is $$ -\begin{align} - E(\mathbf{x}, \mathbf{h}) = - \sum_i^M x_i a_i- \sum_j^N b_j h_j - \sum_{i,j}^{M,N} x_i w_{ij} h_j, -\tag{3} -\end{align} +E(\hat{x},\hat{h}) = -\sum_{ia}^{NA}b_i^a \alpha_i^a(x_i)-\sum_{jd}^{MD}c_j^d \beta_j^d(h_j)-\sum_{ijad}^{NAMD}b_i^a \alpha_i^a(x_i)c_j^d \beta_j^d(h_j)w_{ij}^{ad}. $$ -where the binary values taken on by the nodes are most commonly 0 and 1. -

    -
    - -
    -
    -

    -

    -Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary: -$$ -\begin{align} - E(\mathbf{x}, \mathbf{h}) = \sum_i^M \frac{(x_i - a_i)^2}{2\sigma_i^2} - \sum_j^N b_j h_j - \sum_{i,j}^{M,N} \frac{x_i w_{ij} h_j}{\sigma_i^2}. -\tag{4} -\end{align} -$$ -

    -
    +Here \( \beta_j^d(h_j) \) and \( \alpha_i^a(x_j) \) are so-called transfer functions that map a given input value to a desired feature value. The labels \( a \) and \( d \) denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. Note that there is no connection between nodes in a layer. +

    +The quantities \( b \) and \( c \) can be interpreted as the visible and hidden biases, respectively. + +

    +The connection between the nodes in the two layers is given by the weights \( w_{ij} \).

    @@ -321,7 +314,7 @@ $$

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs046.html b/doc/pub/week48/html/._week48-bs046.html index b8fcad8e7..cdcfc7d45 100644 --- a/doc/pub/week48/html/._week48-bs046.html +++ b/doc/pub/week48/html/._week48-bs046.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,22 +263,44 @@ MathJax.Hub.Config({ -

    More about RBMs

    +

    Defining different types of RBMs

    +There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \). -
      -
    1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
      2. -
    2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction
      3. -
    +

    +

    +
    +

    -Other types of units include: +

    +RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows: +$$ +\begin{align} + E(\mathbf{x}, \mathbf{h}) = - \sum_i^M x_i a_i- \sum_j^N b_j h_j - \sum_{i,j}^{M,N} x_i w_{ij} h_j, +\tag{3} +\end{align} +$$ -

      -
    1. Softmax and multinomial units
      2. -
    2. Gaussian visible and hidden units
      3. -
    3. Binomial units
      4. -
    4. Rectified linear units
      5. -
    +where the binary values taken on by the nodes are most commonly 0 and 1. +
    +
    +
    +
    +

    + +

    +Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary: +$$ +\begin{align} + E(\mathbf{x}, \mathbf{h}) = \sum_i^M \frac{(x_i - a_i)^2}{2\sigma_i^2} - \sum_j^N b_j h_j - \sum_{i,j}^{M,N} \frac{x_i w_{ij} h_j}{\sigma_i^2}. +\tag{4} +\end{align} +$$ +

    +
    + + +

    diff --git a/doc/pub/week48/html/._week48-bs047.html b/doc/pub/week48/html/._week48-bs047.html index fdd645298..c74a5e637 100644 --- a/doc/pub/week48/html/._week48-bs047.html +++ b/doc/pub/week48/html/._week48-bs047.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,42 +263,22 @@ MathJax.Hub.Config({ -

    Autoencoders: Overarching view

    +

    More about RBMs

    -

    -Autoencoders are artificial neural networks capable of learning -efficient representations of the input data (these representations are called codings) without -any supervision (i.e., the training set is unlabeled). These codings -typically have a much lower dimensionality than the input data, making -autoencoders useful for dimensionality reduction. +

      +
    1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
      2. +
    2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction
      3. +
    -

    -More importantly, autoencoders act as powerful feature detectors, and -they can be used for unsupervised pretraining of deep neural networks. +Other types of units include: -

    -Lastly, they are capable of randomly generating new data that looks -very similar to the training data; this is called a generative -model. For example, you could train an autoencoder on pictures of -faces, and it would then be able to generate new faces. Surprisingly, -autoencoders work by simply learning to copy their inputs to their -outputs. This may sound like a trivial task, but we will see that -constraining the network in various ways can make it rather -difficult. For example, you can limit the size of the internal -representation, or you can add noise to the inputs and train the -network to recover the original inputs. These constraints prevent the -autoencoder from trivially copying the inputs directly to the outputs, -which forces it to learn efficient ways of representing the data. In -short, the codings are byproducts of the autoencoder’s attempt to -learn the identity function under some constraints. +

      +
    1. Softmax and multinomial units
      2. +
    2. Gaussian visible and hidden units
      3. +
    3. Binomial units
      4. +
    4. Rectified linear units
      5. +
    -

    -Video on autoencoders - -

    -See also A. Geron's textbook, chapter 15. - -

    diff --git a/doc/pub/week48/html/._week48-bs048.html b/doc/pub/week48/html/._week48-bs048.html index 8ab62d4a0..65dc84403 100644 --- a/doc/pub/week48/html/._week48-bs048.html +++ b/doc/pub/week48/html/._week48-bs048.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
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  • What? Me worry? No final exam in this course!
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    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
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  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
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  • What? Me worry? No final exam in this course!
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  • Statistical analysis and optimization of data
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  • Machine learning
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  • Perspective on Machine Learning
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    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
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  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
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  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
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  • Dual learning
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  • Distributed machine learning
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  • Meta learning
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  • The Challenges Facing Machine Learning
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  • Explainable machine learning
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  • Quantum machine learning
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  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
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  • Social machine learning
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  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,24 +263,40 @@ MathJax.Hub.Config({ -

    Bayesian Machine Learning

    +

    Autoencoders: Overarching view

    -This is an important topic if we aim at extracting a probability -distribution. This gives us also a confidence interval and error -estimates. +Autoencoders are artificial neural networks capable of learning +efficient representations of the input data (these representations are called codings) without +any supervision (i.e., the training set is unlabeled). These codings +typically have a much lower dimensionality than the input data, making +autoencoders useful for dimensionality reduction.

    -Bayesian machine learning allows us to encode our prior beliefs about -what those models should look like, independent of what the data tells -us. This is especially useful when we don’t have a ton of data to -confidently learn our model. +More importantly, autoencoders act as powerful feature detectors, and +they can be used for unsupervised pretraining of deep neural networks.

    -Video on Bayesian deep learning +Lastly, they are capable of randomly generating new data that looks +very similar to the training data; this is called a generative +model. For example, you could train an autoencoder on pictures of +faces, and it would then be able to generate new faces. Surprisingly, +autoencoders work by simply learning to copy their inputs to their +outputs. This may sound like a trivial task, but we will see that +constraining the network in various ways can make it rather +difficult. For example, you can limit the size of the internal +representation, or you can add noise to the inputs and train the +network to recover the original inputs. These constraints prevent the +autoencoder from trivially copying the inputs directly to the outputs, +which forces it to learn efficient ways of representing the data. In +short, the codings are byproducts of the autoencoder’s attempt to +learn the identity function under some constraints.

    -See also the slides here. +Video on autoencoders + +

    +See also A. Geron's textbook, chapter 15.

    @@ -303,7 +324,7 @@ See also the 57

  • 58
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs049.html b/doc/pub/week48/html/._week48-bs049.html index 1a4d0bf3a..5f077ec08 100644 --- a/doc/pub/week48/html/._week48-bs049.html +++ b/doc/pub/week48/html/._week48-bs049.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? 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  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
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    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
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  • Resampling
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  • Other courses on Data science and Machine Learning at UiO
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  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
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  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,38 +263,26 @@ MathJax.Hub.Config({ -

    Reinforcement Learning

    +

    Bayesian Machine Learning

    -Reinforcement Learning (RL) is one of the most exciting fields of -Machine Learning today, and also one of the oldest. It has been around -since the 1950s, producing many interesting applications over the -years. +This is an important topic if we aim at extracting a probability +distribution. This gives us also a confidence interval and error +estimates.

    -It studies -how agents take actions based on trial and error, so as to maximize -some notion of cumulative reward in a dynamic system or -environment. Due to its generality, the problem has also been studied -in many other disciplines, such as game theory, control theory, -operations research, information theory, multi-agent systems, swarm -intelligence, statistics, and genetic algorithms. +Bayesian machine learning allows us to encode our prior beliefs about +what those models should look like, independent of what the data tells +us. This is especially useful when we don’t have a ton of data to +confidently learn our model.

    -In March 2016, AlphaGo, a computer program that plays the board game -Go, beat Lee Sedol in a five-game match. This was the first time a -computer Go program had beaten a 9-dan (highest rank) professional -without handicaps. AlphaGo is based on deep convolutional neural -networks and reinforcement learning. AlphaGo’s victory was a major -milestone in artificial intelligence and it has also made -reinforcement learning a hot research area in the field of machine -learning. +Video on Bayesian deep learning

    -Lecture on Reinforcement Learning. +See also the slides here.

    -See also A. Geron's textbook, chapter 16.

    diff --git a/doc/pub/week48/html/._week48-bs050.html b/doc/pub/week48/html/._week48-bs050.html index 6f1195065..2ef2ad2aa 100644 --- a/doc/pub/week48/html/._week48-bs050.html +++ b/doc/pub/week48/html/._week48-bs050.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,22 +263,38 @@ MathJax.Hub.Config({ -

    Transfer learning

    +

    Reinforcement Learning

    -The goal of transfer learning is to transfer the model or knowledge -obtained from a source task to the target task, in order to resolve -the issues of insufficient training data in the target task. The -rationality of doing so lies in that usually the source and target -tasks have inter-correlations, and therefore either the features, -samples, or models in the source task might provide useful information -for us to better solve the target task. Transfer learning is a hot -research topic in recent years, with many problems still waiting to be studied. +Reinforcement Learning (RL) is one of the most exciting fields of +Machine Learning today, and also one of the oldest. It has been around +since the 1950s, producing many interesting applications over the +years.

    -Lecture on transfer learning. +It studies +how agents take actions based on trial and error, so as to maximize +some notion of cumulative reward in a dynamic system or +environment. Due to its generality, the problem has also been studied +in many other disciplines, such as game theory, control theory, +operations research, information theory, multi-agent systems, swarm +intelligence, statistics, and genetic algorithms.

    +In March 2016, AlphaGo, a computer program that plays the board game +Go, beat Lee Sedol in a five-game match. This was the first time a +computer Go program had beaten a 9-dan (highest rank) professional +without handicaps. AlphaGo is based on deep convolutional neural +networks and reinforcement learning. AlphaGo’s victory was a major +milestone in artificial intelligence and it has also made +reinforcement learning a hot research area in the field of machine +learning. + +

    +Lecture on Reinforcement Learning. + +

    +See also A. Geron's textbook, chapter 16.

    diff --git a/doc/pub/week48/html/._week48-bs051.html b/doc/pub/week48/html/._week48-bs051.html index 5a0e88140..bbd632634 100644 --- a/doc/pub/week48/html/._week48-bs051.html +++ b/doc/pub/week48/html/._week48-bs051.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,21 +263,20 @@ MathJax.Hub.Config({ -

    Adversarial learning

    +

    Transfer learning

    -The conventional deep generative model has a potential problem: the -model tends to generate extreme instances to maximize the -probabilistic likelihood, which will hurt its performance. Adversarial -learning utilizes the adversarial behaviors (e.g., generating -adversarial instances or training an adversarial model) to enhance the -robustness of the model and improve the quality of the generated -data. In recent years, one of the most promising unsupervised learning -technologies, generative adversarial networks (GAN), has already been -successfully applied to image, speech, and text. +The goal of transfer learning is to transfer the model or knowledge +obtained from a source task to the target task, in order to resolve +the issues of insufficient training data in the target task. The +rationality of doing so lies in that usually the source and target +tasks have inter-correlations, and therefore either the features, +samples, or models in the source task might provide useful information +for us to better solve the target task. Transfer learning is a hot +research topic in recent years, with many problems still waiting to be studied.

    -Lecture on adversial learning. +Lecture on transfer learning.

    @@ -300,7 +304,7 @@ successfully applied to image, speech, and text.

  • 60
  • 61
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs052.html b/doc/pub/week48/html/._week48-bs052.html index c10283e58..9f27d7836 100644 --- a/doc/pub/week48/html/._week48-bs052.html +++ b/doc/pub/week48/html/._week48-bs052.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,18 +263,21 @@ MathJax.Hub.Config({ -

    Dual learning

    +

    Adversarial learning

    -Dual learning is a new learning paradigm, the basic idea of which is -to use the primal-dual structure between machine learning tasks to -obtain effective feedback/regularization, and guide and strengthen the -learning process, thus reducing the requirement of large-scale labeled -data for deep learning. The idea of dual learning has been applied to -many problems in machine learning, including machine translation, -image style conversion, question answering and generation, image -classification and generation, text classification and generation, -image-to-text, and text-to-image. +The conventional deep generative model has a potential problem: the +model tends to generate extreme instances to maximize the +probabilistic likelihood, which will hurt its performance. Adversarial +learning utilizes the adversarial behaviors (e.g., generating +adversarial instances or training an adversarial model) to enhance the +robustness of the model and improve the quality of the generated +data. In recent years, one of the most promising unsupervised learning +technologies, generative adversarial networks (GAN), has already been +successfully applied to image, speech, and text. + +

    +Lecture on adversial learning.

    @@ -297,7 +305,7 @@ image-to-text, and text-to-image.

  • 61
  • 62
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs053.html b/doc/pub/week48/html/._week48-bs053.html index c67c70023..a9b3de55e 100644 --- a/doc/pub/week48/html/._week48-bs053.html +++ b/doc/pub/week48/html/._week48-bs053.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,13 +263,18 @@ MathJax.Hub.Config({ -

    Distributed machine learning

    +

    Dual learning

    -Distributed computation will speed up machine learning algorithms, -significantly improve their efficiency, and thus enlarge their -application. When distributed meets machine learning, more than just -implementing the machine learning algorithms in parallel is required. +Dual learning is a new learning paradigm, the basic idea of which is +to use the primal-dual structure between machine learning tasks to +obtain effective feedback/regularization, and guide and strengthen the +learning process, thus reducing the requirement of large-scale labeled +data for deep learning. The idea of dual learning has been applied to +many problems in machine learning, including machine translation, +image style conversion, question answering and generation, image +classification and generation, text classification and generation, +image-to-text, and text-to-image.

    @@ -292,7 +302,7 @@ implementing the machine learning algorithms in parallel is required.

  • 62
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs054.html b/doc/pub/week48/html/._week48-bs054.html index df1381181..13c664bb6 100644 --- a/doc/pub/week48/html/._week48-bs054.html +++ b/doc/pub/week48/html/._week48-bs054.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,16 +263,13 @@ MathJax.Hub.Config({ -

    Meta learning

    +

    Distributed machine learning

    -Meta learning is an emerging research direction in machine -learning. Roughly speaking, meta learning concerns learning how to -learn, and focuses on the understanding and adaptation of the learning -itself, instead of just completing a specific learning task. That is, -a meta learner needs to be able to evaluate its own learning methods -and adjust its own learning methods according to specific learning -tasks. +Distributed computation will speed up machine learning algorithms, +significantly improve their efficiency, and thus enlarge their +application. When distributed meets machine learning, more than just +implementing the machine learning algorithms in parallel is required.

    @@ -294,6 +296,8 @@ tasks.

  • 62
  • 63
  • 64
    • +
  • ...
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs055.html b/doc/pub/week48/html/._week48-bs055.html index 7dbe7c84d..1d07e5f4b 100644 --- a/doc/pub/week48/html/._week48-bs055.html +++ b/doc/pub/week48/html/._week48-bs055.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,27 +263,16 @@ MathJax.Hub.Config({ -

    The Challenges Facing Machine Learning

    +

    Meta learning

    -While there has been much progress in machine learning, there are also challenges. - -

    -For example, the mainstream machine learning technologies are -black-box approaches, making us concerned about their potential -risks. To tackle this challenge, we may want to make machine learning -more explainable and controllable. As another example, the -computational complexity of machine learning algorithms is usually -very high and we may want to invent lightweight algorithms or -implementations. Furthermore, in many domains such as physics, -chemistry, biology, and social sciences, people usually seek elegantly -simple equations (e.g., the Schrödinger equation) to uncover the -underlying laws behind various phenomena. In the field of machine -learning, can we reveal simple laws instead of designing more complex -models for data fitting? Although there are many challenges, we are -still very optimistic about the future of machine learning. As we look -forward to the future, here are what we think the research hotspots in -the next ten years will be. +Meta learning is an emerging research direction in machine +learning. Roughly speaking, meta learning concerns learning how to +learn, and focuses on the understanding and adaptation of the learning +itself, instead of just completing a specific learning task. That is, +a meta learner needs to be able to evaluate its own learning methods +and adjust its own learning methods according to specific learning +tasks.

    @@ -304,6 +298,7 @@ the next ten years will be.

  • 62
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    • +
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs056.html b/doc/pub/week48/html/._week48-bs056.html index 93dc8e732..ca336d01c 100644 --- a/doc/pub/week48/html/._week48-bs056.html +++ b/doc/pub/week48/html/._week48-bs056.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
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  • What? Me worry? No final exam in this course!
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  • Statistical analysis and optimization of data
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  • Which Activation and Weights to Choose in Neural Networks
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  • Optimization Methods and Hyperparameters
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  • Types of Machine Learning, a repetition
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  • Some similarities and differences from DNNs
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  • Boltzmann machines (BM)
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  • The structure of the RBM network
    • -
  • The network
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  • Goals
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  • Joint distribution
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  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,27 +263,27 @@ MathJax.Hub.Config({ -

    Explainable machine learning

    +

    The Challenges Facing Machine Learning

    -Machine learning, especially deep learning, evolves rapidly. The -ability gap between machine and human on many complex cognitive tasks -becomes narrower and narrower. However, we are still in the very early -stage in terms of explaining why those effective models work and how -they work. +While there has been much progress in machine learning, there are also challenges.

    -What is missing: the gap between correlation and causation. Standard Machine Learning is based on what e have called a frequentist approach. - -

    -Most -machine learning techniques, especially the statistical ones, depend -highly on correlations in data sets to make predictions and analyses. In -contrast, rational humans tend to reply on clear and trustworthy -causality relations obtained via logical reasoning on real and clear -facts. It is one of the core goals of explainable machine learning to -transition from solving problems by data correlation to solving -problems by logical reasoning. Bayesian Machine Learning is one of the exciting research directions in this field. +For example, the mainstream machine learning technologies are +black-box approaches, making us concerned about their potential +risks. To tackle this challenge, we may want to make machine learning +more explainable and controllable. As another example, the +computational complexity of machine learning algorithms is usually +very high and we may want to invent lightweight algorithms or +implementations. Furthermore, in many domains such as physics, +chemistry, biology, and social sciences, people usually seek elegantly +simple equations (e.g., the Schrödinger equation) to uncover the +underlying laws behind various phenomena. In the field of machine +learning, can we reveal simple laws instead of designing more complex +models for data fitting? Although there are many challenges, we are +still very optimistic about the future of machine learning. As we look +forward to the future, here are what we think the research hotspots in +the next ten years will be.

    @@ -303,6 +308,7 @@ problems by logical reasoning. Bayesian Machine Learning is one of the exciting

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    • diff --git a/doc/pub/week48/html/._week48-bs057.html b/doc/pub/week48/html/._week48-bs057.html index 718637db8..76e43c73e 100644 --- a/doc/pub/week48/html/._week48-bs057.html +++ b/doc/pub/week48/html/._week48-bs057.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,33 +263,27 @@ MathJax.Hub.Config({ -

    Quantum machine learning

    +

    Explainable machine learning

    -Quantum machine learning is an emerging interdisciplinary research -area at the intersection of quantum computing and machine learning. +Machine learning, especially deep learning, evolves rapidly. The +ability gap between machine and human on many complex cognitive tasks +becomes narrower and narrower. However, we are still in the very early +stage in terms of explaining why those effective models work and how +they work.

    -Quantum computers use effects such as quantum coherence and quantum -entanglement to process information, which is fundamentally different -from classical computers. Quantum algorithms have surpassed the best -classical algorithms in several problems (e.g., searching for an -unsorted database, inverting a sparse matrix), which we call quantum -acceleration. +What is missing: the gap between correlation and causation. Standard Machine Learning is based on what e have called a frequentist approach.

    -When quantum computing meets machine learning, it can be a mutually -beneficial and reinforcing process, as it allows us to take advantage -of quantum computing to improve the performance of classical machine -learning algorithms. In addition, we can also use the machine learning -algorithms (on classic computers) to analyze and improve quantum -computing systems. - -

    -Lecture on Quantum ML. - -

    -Read interview with Maria Schuld on her work on Quantum Machine Learning. See also her recent textbook. +Most +machine learning techniques, especially the statistical ones, depend +highly on correlations in data sets to make predictions and analyses. In +contrast, rational humans tend to reply on clear and trustworthy +causality relations obtained via logical reasoning on real and clear +facts. It is one of the core goals of explainable machine learning to +transition from solving problems by data correlation to solving +problems by logical reasoning. Bayesian Machine Learning is one of the exciting research directions in this field.

    @@ -308,6 +307,7 @@ computing systems.

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs058.html b/doc/pub/week48/html/._week48-bs058.html index 120ca9bb1..9ffbac93c 100644 --- a/doc/pub/week48/html/._week48-bs058.html +++ b/doc/pub/week48/html/._week48-bs058.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
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  • Perspective on Machine Learning
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  • Machine Learning Research
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  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,21 +263,33 @@ MathJax.Hub.Config({ -

    Quantum machine learning algorithms based on linear algebra

    +

    Quantum machine learning

    -Many quantum machine learning algorithms are based on variants of -quantum algorithms for solving linear equations, which can efficiently -solve N-variable linear equations with complexity of O(log2 N) under -certain conditions. The quantum matrix inversion algorithm can -accelerate many machine learning methods, such as least square linear -regression, least square version of support vector machine, Gaussian -process, and more. The training of these algorithms can be simplified -to solve linear equations. The key bottleneck of this type of quantum -machine learning algorithms is data input—that is, how to initialize -the quantum system with the entire data set. Although efficient -data-input algorithms exist for certain situations, how to efficiently -input data into a quantum system is as yet unknown for most cases. +Quantum machine learning is an emerging interdisciplinary research +area at the intersection of quantum computing and machine learning. + +

    +Quantum computers use effects such as quantum coherence and quantum +entanglement to process information, which is fundamentally different +from classical computers. Quantum algorithms have surpassed the best +classical algorithms in several problems (e.g., searching for an +unsorted database, inverting a sparse matrix), which we call quantum +acceleration. + +

    +When quantum computing meets machine learning, it can be a mutually +beneficial and reinforcing process, as it allows us to take advantage +of quantum computing to improve the performance of classical machine +learning algorithms. In addition, we can also use the machine learning +algorithms (on classic computers) to analyze and improve quantum +computing systems. + +

    +Lecture on Quantum ML. + +

    +Read interview with Maria Schuld on her work on Quantum Machine Learning. See also her recent textbook.

    @@ -295,6 +312,7 @@ input data into a quantum system is as yet unknown for most cases.

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs059.html b/doc/pub/week48/html/._week48-bs059.html index 10bf653ab..3304cec8f 100644 --- a/doc/pub/week48/html/._week48-bs059.html +++ b/doc/pub/week48/html/._week48-bs059.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,16 +263,21 @@ MathJax.Hub.Config({ -

    Quantum reinforcement learning

    +

    Quantum machine learning algorithms based on linear algebra

    -In quantum reinforcement learning, a quantum agent interacts with the -classical environment to obtain rewards from the environment, so as to -adjust and improve its behavioral strategies. In some cases, it -achieves quantum acceleration by the quantum processing capabilities -of the agent or the possibility of exploring the environment through -quantum superposition. Such algorithms have been proposed in -superconducting circuits and systems of trapped ions. +Many quantum machine learning algorithms are based on variants of +quantum algorithms for solving linear equations, which can efficiently +solve N-variable linear equations with complexity of O(log2 N) under +certain conditions. The quantum matrix inversion algorithm can +accelerate many machine learning methods, such as least square linear +regression, least square version of support vector machine, Gaussian +process, and more. The training of these algorithms can be simplified +to solve linear equations. The key bottleneck of this type of quantum +machine learning algorithms is data input—that is, how to initialize +the quantum system with the entire data set. Although efficient +data-input algorithms exist for certain situations, how to efficiently +input data into a quantum system is as yet unknown for most cases.

    @@ -289,6 +299,7 @@ superconducting circuits and systems of trapped ions.

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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs060.html b/doc/pub/week48/html/._week48-bs060.html index e2a72ab3e..1fa2343d8 100644 --- a/doc/pub/week48/html/._week48-bs060.html +++ b/doc/pub/week48/html/._week48-bs060.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,21 +263,16 @@ MathJax.Hub.Config({ -

    Quantum deep learning

    +

    Quantum reinforcement learning

    -Dedicated quantum information processors, such as quantum annealers -and programmable photonic circuits, are well suited for building deep -quantum networks. The simplest deep quantum network is the Boltzmann -machine. The classical Boltzmann machine consists of bits with tunable -interactions and is trained by adjusting the interaction of these bits -so that the distribution of its expression conforms to the statistics -of the data. To quantize the Boltzmann machine, the neural network can -simply be represented as a set of interacting quantum spins that -correspond to an adjustable Ising model. Then, by initializing the -input neurons in the Boltzmann machine to a fixed state and allowing -the system to heat up, we can read out the output qubits to get the -result. +In quantum reinforcement learning, a quantum agent interacts with the +classical environment to obtain rewards from the environment, so as to +adjust and improve its behavioral strategies. In some cases, it +achieves quantum acceleration by the quantum processing capabilities +of the agent or the possibility of exploring the environment through +quantum superposition. Such algorithms have been proposed in +superconducting circuits and systems of trapped ions.

    @@ -293,6 +293,7 @@ result.

  • 62
  • 63
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs061.html b/doc/pub/week48/html/._week48-bs061.html index ff31bb808..44c25d4e5 100644 --- a/doc/pub/week48/html/._week48-bs061.html +++ b/doc/pub/week48/html/._week48-bs061.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,20 +263,21 @@ MathJax.Hub.Config({ -

    Social machine learning

    +

    Quantum deep learning

    -Machine learning aims to imitate how humans -learn. While we have developed successful machine learning algorithms, -until now we have ignored one important fact: humans are social. Each -of us is one part of the total society and it is difficult for us to -live, learn, and improve ourselves, alone and isolated. Therefore, we -should design machines with social properties. Can we let machines -evolve by imitating human society so as to achieve more effective, -intelligent, interpretable “social machine learning”? - -

    -And much more. +Dedicated quantum information processors, such as quantum annealers +and programmable photonic circuits, are well suited for building deep +quantum networks. The simplest deep quantum network is the Boltzmann +machine. The classical Boltzmann machine consists of bits with tunable +interactions and is trained by adjusting the interaction of these bits +so that the distribution of its expression conforms to the statistics +of the data. To quantize the Boltzmann machine, the neural network can +simply be represented as a set of interacting quantum spins that +correspond to an adjustable Ising model. Then, by initializing the +input neurons in the Boltzmann machine to a fixed state and allowing +the system to heat up, we can read out the output qubits to get the +result.

    @@ -291,6 +297,7 @@ And much more.

  • 62
  • 63
  • 64
    • +
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  • »
    • diff --git a/doc/pub/week48/html/._week48-bs062.html b/doc/pub/week48/html/._week48-bs062.html index ef08354ba..14420f92a 100644 --- a/doc/pub/week48/html/._week48-bs062.html +++ b/doc/pub/week48/html/._week48-bs062.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,14 +263,20 @@ MathJax.Hub.Config({ -

    The last words?

    +

    Social machine learning

    -Early computer scientist Alan Kay said, The best way to predict the -future is to create it. Therefore, all machine learning -practitioners, whether scholars or engineers, professors or students, -need to work together to advance these important research -topics. Together, we will not just predict the future, but create it. +Machine learning aims to imitate how humans +learn. While we have developed successful machine learning algorithms, +until now we have ignored one important fact: humans are social. Each +of us is one part of the total society and it is difficult for us to +live, learn, and improve ourselves, alone and isolated. Therefore, we +should design machines with social properties. Can we let machines +evolve by imitating human society so as to achieve more effective, +intelligent, interpretable “social machine learning”? + +

    +And much more.

    @@ -284,6 +295,7 @@ topics. Together, we will not just predict the future, but create it.

  • 62
  • 63
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/._week48-bs063.html b/doc/pub/week48/html/._week48-bs063.html index 4e5b813e7..705392c3d 100644 --- a/doc/pub/week48/html/._week48-bs063.html +++ b/doc/pub/week48/html/._week48-bs063.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -258,13 +263,16 @@ MathJax.Hub.Config({ -

    Best wishes to you all and thanks so much for your heroic efforts this semester

    +

    The last words?

    -



    +Early computer scientist Alan Kay said, The best way to predict the +future is to create it. Therefore, all machine learning +practitioners, whether scholars or engineers, professors or students, +need to work together to advance these important research +topics. Together, we will not just predict the future, but create it.

    -

    diff --git a/doc/pub/week48/html/week48-bs.html b/doc/pub/week48/html/week48-bs.html index dc68b9446..a8d8e7df6 100644 --- a/doc/pub/week48/html/week48-bs.html +++ b/doc/pub/week48/html/week48-bs.html @@ -71,78 +71,82 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -196,53 +200,54 @@ MathJax.Hub.Config({
  • Back to the more realistic cases
  • Setting up the matrices and the problem
  • Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
    • -
  • Summary of course
    • -
  • What? Me worry? No final exam in this course!
    • -
  • Topics we have covered this year
    • -
  • Statistical analysis and optimization of data
    • -
  • Machine learning
    • -
  • Learning outcomes and overarching aims of this course
    • -
  • Perspective on Machine Learning
    • -
  • Machine Learning Research
    • -
  • Starting your Machine Learning Project
    • -
  • Choose a Model and Algorithm
    • -
  • Preparing Your Data
    • -
  • Which Activation and Weights to Choose in Neural Networks
    • -
  • Optimization Methods and Hyperparameters
    • -
  • Resampling
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Additional courses of interest
    • -
  • What's the future like?
    • -
  • Types of Machine Learning, a repetition
    • -
  • Why Boltzmann machines?
    • -
  • Boltzmann Machines
    • -
  • Some similarities and differences from DNNs
    • -
  • Boltzmann machines (BM)
    • -
  • A standard BM setup
    • -
  • The structure of the RBM network
    • -
  • The network
    • -
  • Goals
    • -
  • Joint distribution
    • -
  • Network Elements, the energy function
    • -
  • Defining different types of RBMs
    • -
  • More about RBMs
    • -
  • Autoencoders: Overarching view
    • -
  • Bayesian Machine Learning
    • -
  • Reinforcement Learning
    • -
  • Transfer learning
    • -
  • Adversarial learning
    • -
  • Dual learning
    • -
  • Distributed machine learning
    • -
  • Meta learning
    • -
  • The Challenges Facing Machine Learning
    • -
  • Explainable machine learning
    • -
  • Quantum machine learning
    • -
  • Quantum machine learning algorithms based on linear algebra
    • -
  • Quantum reinforcement learning
    • -
  • Quantum deep learning
    • -
  • Social machine learning
    • -
  • The last words?
    • -
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • +
  • SVMs and Regression and multiclass classification
    • +
  • Summary of course
    • +
  • What? Me worry? No final exam in this course!
    • +
  • Topics we have covered this year
    • +
  • Statistical analysis and optimization of data
    • +
  • Machine learning
    • +
  • Learning outcomes and overarching aims of this course
    • +
  • Perspective on Machine Learning
    • +
  • Machine Learning Research
    • +
  • Starting your Machine Learning Project
    • +
  • Choose a Model and Algorithm
    • +
  • Preparing Your Data
    • +
  • Which Activation and Weights to Choose in Neural Networks
    • +
  • Optimization Methods and Hyperparameters
    • +
  • Resampling
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Additional courses of interest
    • +
  • What's the future like?
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
    • +
  • Boltzmann Machines
    • +
  • Some similarities and differences from DNNs
    • +
  • Boltzmann machines (BM)
    • +
  • A standard BM setup
    • +
  • The structure of the RBM network
    • +
  • The network
    • +
  • Goals
    • +
  • Joint distribution
    • +
  • Network Elements, the energy function
    • +
  • Defining different types of RBMs
    • +
  • More about RBMs
    • +
  • Autoencoders: Overarching view
    • +
  • Bayesian Machine Learning
    • +
  • Reinforcement Learning
    • +
  • Transfer learning
    • +
  • Adversarial learning
    • +
  • Dual learning
    • +
  • Distributed machine learning
    • +
  • Meta learning
    • +
  • The Challenges Facing Machine Learning
    • +
  • Explainable machine learning
    • +
  • Quantum machine learning
    • +
  • Quantum machine learning algorithms based on linear algebra
    • +
  • Quantum reinforcement learning
    • +
  • Quantum deep learning
    • +
  • Social machine learning
    • +
  • The last words?
    • +
  • Best wishes to you all and thanks so much for your heroic efforts this semester
    • @@ -301,7 +306,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 64
    • +
  • 65
  • »
    • diff --git a/doc/pub/week48/html/week48-reveal.html b/doc/pub/week48/html/week48-reveal.html index a8e9d41b4..836f6f418 100644 --- a/doc/pub/week48/html/week48-reveal.html +++ b/doc/pub/week48/html/week48-reveal.html @@ -862,18 +862,26 @@ And then we are ready to go.
    -

    Summary of course

    +

    SVMs and Regression and multiclass classification

    + +

    +See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.

    -

    What? Me worry? No final exam in this course!

    +

    Summary of course

    +
    + + +
    +

    What? Me worry? No final exam in this course!





    -

    Topics we have covered this year

    +

    Topics we have covered this year

    The course has two central parts @@ -886,7 +894,7 @@ The course has two central parts

    -

    Statistical analysis and optimization of data

    +

    Statistical analysis and optimization of data

    The following topics be covered @@ -904,7 +912,7 @@ The following topics be covered

    -

    Machine learning

    +

    Machine learning

    The following topics will be covered @@ -946,7 +954,7 @@ The following topics will be covered

    -

    Learning outcomes and overarching aims of this course

    +

    Learning outcomes and overarching aims of this course

    The course introduces a variety of central algorithms and methods @@ -973,7 +981,7 @@ ethical conduct is emphasized throughout the course.

    -

    Perspective on Machine Learning

    +

    Perspective on Machine Learning

    1. Rapidly emerging application area
      2. @@ -991,7 +999,7 @@ Neural Networks, etc.
      -

      Machine Learning Research

      +

      Machine Learning Research

      Where to find recent results: @@ -1007,7 +1015,7 @@ Where to find recent results:

      -

      Starting your Machine Learning Project

      +

      Starting your Machine Learning Project

      1. Identify problem type: classification, generation, regression
        2. @@ -1020,7 +1028,7 @@ Where to find recent results:
        -

        Choose a Model and Algorithm

        +

        Choose a Model and Algorithm

        1. Supervised?
          2. @@ -1031,7 +1039,7 @@ Where to find recent results:
          -

          Preparing Your Data

          +

          Preparing Your Data

          1. Shuffle your data
            2. @@ -1061,7 +1069,7 @@ Where to find recent results:
            -

            Which Activation and Weights to Choose in Neural Networks

            +

            Which Activation and Weights to Choose in Neural Networks

            1. RELU? ELU?
              2. @@ -1084,7 +1092,7 @@ Where to find recent results:
              -

              Optimization Methods and Hyperparameters

              +

              Optimization Methods and Hyperparameters

              1. Stochastic gradient descent @@ -1109,7 +1117,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie
                -

                Resampling

                +

                Resampling

                When do we resample? @@ -1123,7 +1131,7 @@ When do we resample?

                -

                Other courses on Data science and Machine Learning at UiO

                +

                Other courses on Data science and Machine Learning at UiO

                The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1142,7 +1150,7 @@ The link here Additional courses of interest +

                Additional courses of interest

                1. STK4051 Computational Statistics
                  2. @@ -1152,7 +1160,7 @@ The link here What's the future like? +

                  What's the future like?

                  Based on multi-layer nonlinear neural networks, deep learning can @@ -1176,7 +1184,7 @@ networks have been proposed, such as

                  -

                  Types of Machine Learning, a repetition

                  +

                  Types of Machine Learning, a repetition

                  @@ -1209,7 +1217,7 @@ Some of the most common tasks are:
                  -

                  Why Boltzmann machines?

                  +

                  Why Boltzmann machines?

                  What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1224,7 +1232,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                  Boltzmann Machines

                  Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1244,7 +1252,7 @@ Why use a generative model rather than the more well known discriminative deep n

                  -

                  Some similarities and differences from DNNs

                  +

                  Some similarities and differences from DNNs

                  1. Both use gradient-descent based learning procedures for minimizing cost functions
                    2. @@ -1258,7 +1266,7 @@ History: The RBM was developed by amongst others                     Boltzmann machines (BM) +

                    Boltzmann machines (BM)

                    @@ -1289,7 +1297,7 @@ the learned distribution.
                    -

                    A standard BM setup

                    +

                    A standard BM setup

                    @@ -1319,7 +1327,7 @@ Here we take away all lateral connections between nodes in the visible layer as
                    -

                    The structure of the RBM network

                    +

                    The structure of the RBM network





                    @@ -1327,7 +1335,7 @@ Here we take away all lateral connections between nodes in the visible layer as
                    -

                    The network

                    +

                    The network

                    The network layers: @@ -1340,7 +1348,7 @@ Here we take away all lateral connections between nodes in the visible layer as

                    -

                    Goals

                    +

                    Goals

                    The goal of the hidden layer is to increase the model's expressive @@ -1362,7 +1370,7 @@ over (integrated out).

                    -

                    Joint distribution

                    +

                    Joint distribution

                    The restricted Boltzmann machine is described by a Bolztmann distribution @@ -1390,7 +1398,7 @@ It is common to ignore \( T_0 \) by setting it to one.

                    -

                    Network Elements, the energy function

                    +

                    Network Elements, the energy function

                    The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1420,7 +1428,7 @@ The connection between the nodes in the two layers is given by the weights \( w_

                    -

                    Defining different types of RBMs

                    +

                    Defining different types of RBMs

                    There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                    @@ -1457,7 +1465,7 @@ $$

                    -

                    More about RBMs

                    +

                    More about RBMs

                    1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                      2. @@ -1477,7 +1485,7 @@ Other types of units include:
                      -

                      Autoencoders: Overarching view

                      +

                      Autoencoders: Overarching view

                      Autoencoders are artificial neural networks capable of learning @@ -1515,7 +1523,7 @@ See also A. Geron's textbook, chapter 15.

                      -

                      Bayesian Machine Learning

                      +

                      Bayesian Machine Learning

                      This is an important topic if we aim at extracting a probability @@ -1537,7 +1545,7 @@ See also the Reinforcement Learning +

                      Reinforcement Learning

                      Reinforcement Learning (RL) is one of the most exciting fields of @@ -1573,7 +1581,7 @@ See also A. Geron's textbook, chapter 16.

                      -

                      Transfer learning

                      +

                      Transfer learning

                      The goal of transfer learning is to transfer the model or knowledge @@ -1591,7 +1599,7 @@ research topic in recent years, with many problems still waiting to be studied.

                      -

                      Adversarial learning

                      +

                      Adversarial learning

                      The conventional deep generative model has a potential problem: the @@ -1610,7 +1618,7 @@ successfully applied to image, speech, and text.

                      -

                      Dual learning

                      +

                      Dual learning

                      Dual learning is a new learning paradigm, the basic idea of which is @@ -1626,7 +1634,7 @@ image-to-text, and text-to-image.

                      -

                      Distributed machine learning

                      +

                      Distributed machine learning

                      Distributed computation will speed up machine learning algorithms, @@ -1637,7 +1645,7 @@ implementing the machine learning algorithms in parallel is required.

                      -

                      Meta learning

                      +

                      Meta learning

                      Meta learning is an emerging research direction in machine @@ -1651,7 +1659,7 @@ tasks.

                      -

                      The Challenges Facing Machine Learning

                      +

                      The Challenges Facing Machine Learning

                      While there has been much progress in machine learning, there are also challenges. @@ -1676,7 +1684,7 @@ the next ten years will be.

                      -

                      Explainable machine learning

                      +

                      Explainable machine learning

                      Machine learning, especially deep learning, evolves rapidly. The @@ -1701,7 +1709,7 @@ problems by logical reasoning. Bayesian Machine Learning is one of the exciting

                      -

                      Quantum machine learning

                      +

                      Quantum machine learning

                      Quantum machine learning is an emerging interdisciplinary research @@ -1732,7 +1740,7 @@ computing systems.

                      -

                      Quantum machine learning algorithms based on linear algebra

                      +

                      Quantum machine learning algorithms based on linear algebra

                      Many quantum machine learning algorithms are based on variants of @@ -1751,7 +1759,7 @@ input data into a quantum system is as yet unknown for most cases.

                      -

                      Quantum reinforcement learning

                      +

                      Quantum reinforcement learning

                      In quantum reinforcement learning, a quantum agent interacts with the @@ -1765,7 +1773,7 @@ superconducting circuits and systems of trapped ions.

                      -

                      Quantum deep learning

                      +

                      Quantum deep learning

                      Dedicated quantum information processors, such as quantum annealers @@ -1784,7 +1792,7 @@ result.

                      -

                      Social machine learning

                      +

                      Social machine learning

                      Machine learning aims to imitate how humans @@ -1802,7 +1810,7 @@ And much more.

                      -

                      The last words?

                      +

                      The last words?

                      Early computer scientist Alan Kay said, The best way to predict the @@ -1814,7 +1822,7 @@ topics. Together, we will not just predict the future, but create it.

                      -

                      Best wishes to you all and thanks so much for your heroic efforts this semester

                      +

                      Best wishes to you all and thanks so much for your heroic efforts this semester





                      diff --git a/doc/pub/week48/html/week48-solarized.html b/doc/pub/week48/html/week48-solarized.html index 5cabd62ef..ac04f953f 100644 --- a/doc/pub/week48/html/week48-solarized.html +++ b/doc/pub/week48/html/week48-solarized.html @@ -91,78 +91,82 @@ div { text-align: justify; text-justify: inter-word; } 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -866,18 +870,26 @@ And then we are ready to go.











                      -

                      Summary of course

                      +

                      SVMs and Regression and multiclass classification

                      + +

                      +See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.











                      -

                      What? Me worry? No final exam in this course!

                      +

                      Summary of course

                      + +

                      +









                      + +

                      What? Me worry? No final exam in this course!















                      -

                      Topics we have covered this year

                      +

                      Topics we have covered this year

                      The course has two central parts @@ -889,7 +901,7 @@ The course has two central parts









                      -

                      Statistical analysis and optimization of data

                      +

                      Statistical analysis and optimization of data

                      The following topics be covered @@ -906,7 +918,7 @@ The following topics be covered









                      -

                      Machine learning

                      +

                      Machine learning

                      The following topics will be covered @@ -950,7 +962,7 @@ The following topics will be covered









                      -

                      Learning outcomes and overarching aims of this course

                      +

                      Learning outcomes and overarching aims of this course

                      The course introduces a variety of central algorithms and methods @@ -976,7 +988,7 @@ ethical conduct is emphasized throughout the course.









                      -

                      Perspective on Machine Learning

                      +

                      Perspective on Machine Learning

                      1. Rapidly emerging application area
                        2. @@ -993,7 +1005,7 @@ Neural Networks, etc.











                        -

                        Machine Learning Research

                        +

                        Machine Learning Research

                        Where to find recent results: @@ -1008,7 +1020,7 @@ Where to find recent results:









                        -

                        Starting your Machine Learning Project

                        +

                        Starting your Machine Learning Project

                        1. Identify problem type: classification, generation, regression
                          2. @@ -1020,7 +1032,7 @@ Where to find recent results:









                          -

                          Choose a Model and Algorithm

                          +

                          Choose a Model and Algorithm

                          1. Supervised?
                            2. @@ -1030,7 +1042,7 @@ Where to find recent results:









                            -

                            Preparing Your Data

                            +

                            Preparing Your Data

                            1. Shuffle your data
                              2. @@ -1058,7 +1070,7 @@ Where to find recent results:









                              -

                              Which Activation and Weights to Choose in Neural Networks

                              +

                              Which Activation and Weights to Choose in Neural Networks

                              1. RELU? ELU?
                                2. @@ -1079,7 +1091,7 @@ Where to find recent results:









                                -

                                Optimization Methods and Hyperparameters

                                +

                                Optimization Methods and Hyperparameters

                                1. Stochastic gradient descent @@ -1102,7 +1114,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie











                                  -

                                  Resampling

                                  +

                                  Resampling

                                  When do we resample? @@ -1115,7 +1127,7 @@ When do we resample?









                                  -

                                  Other courses on Data science and Machine Learning at UiO

                                  +

                                  Other courses on Data science and Machine Learning at UiO

                                  The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1133,7 +1145,7 @@ The link here Additional courses of interest +

                                  Additional courses of interest

                                  1. STK4051 Computational Statistics
                                    2. @@ -1142,7 +1154,7 @@ The link here                                     What's the future like? +

                                    What's the future like?

                                    Based on multi-layer nonlinear neural networks, deep learning can @@ -1165,7 +1177,7 @@ networks have been proposed, such as









                                    -

                                    Types of Machine Learning, a repetition

                                    +

                                    Types of Machine Learning, a repetition

                                    @@ -1195,7 +1207,7 @@ Some of the most common tasks are:











                                    -

                                    Why Boltzmann machines?

                                    +

                                    Why Boltzmann machines?

                                    What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1210,7 +1222,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                                    Boltzmann Machines

                                    Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1229,7 +1241,7 @@ Why use a generative model rather than the more well known discriminative deep n









                                    -

                                    Some similarities and differences from DNNs

                                    +

                                    Some similarities and differences from DNNs

                                    1. Both use gradient-descent based learning procedures for minimizing cost functions
                                      2. @@ -1242,7 +1254,7 @@ History: The RBM was developed by amongst others                                       Boltzmann machines (BM) +

                                      Boltzmann machines (BM)

                                      @@ -1274,7 +1286,7 @@ the learned distribution.











                                      -

                                      A standard BM setup

                                      +

                                      A standard BM setup

                                      @@ -1307,7 +1319,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                      -

                                      The structure of the RBM network

                                      +

                                      The structure of the RBM network





                                      @@ -1315,7 +1327,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                      -

                                      The network

                                      +

                                      The network

                                      The network layers: @@ -1327,7 +1339,7 @@ Here we take away all lateral connections between nodes in the visible layer as









                                      -

                                      Goals

                                      +

                                      Goals

                                      The goal of the hidden layer is to increase the model's expressive @@ -1348,7 +1360,7 @@ over (integrated out).









                                      -

                                      Joint distribution

                                      +

                                      Joint distribution

                                      The restricted Boltzmann machine is described by a Bolztmann distribution @@ -1372,7 +1384,7 @@ It is common to ignore \( T_0 \) by setting it to one.











                                      -

                                      Network Elements, the energy function

                                      +

                                      Network Elements, the energy function

                                      The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1400,7 +1412,7 @@ The connection between the nodes in the two layers is given by the weights \( w_











                                      -

                                      Defining different types of RBMs

                                      +

                                      Defining different types of RBMs

                                      There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                                      @@ -1438,7 +1450,7 @@ $$











                                      -

                                      More about RBMs

                                      +

                                      More about RBMs

                                      1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                                        2. @@ -1456,7 +1468,7 @@ Other types of units include:









                                        -

                                        Autoencoders: Overarching view

                                        +

                                        Autoencoders: Overarching view

                                        Autoencoders are artificial neural networks capable of learning @@ -1494,7 +1506,7 @@ See also A. Geron's textbook, chapter 15.











                                        -

                                        Bayesian Machine Learning

                                        +

                                        Bayesian Machine Learning

                                        This is an important topic if we aim at extracting a probability @@ -1516,7 +1528,7 @@ See also the Reinforcement Learning +

                                        Reinforcement Learning

                                        Reinforcement Learning (RL) is one of the most exciting fields of @@ -1550,7 +1562,7 @@ learning. See also A. Geron's textbook, chapter 16.









                                        -

                                        Transfer learning

                                        +

                                        Transfer learning

                                        The goal of transfer learning is to transfer the model or knowledge @@ -1568,7 +1580,7 @@ research topic in recent years, with many problems still waiting to be studied.











                                        -

                                        Adversarial learning

                                        +

                                        Adversarial learning

                                        The conventional deep generative model has a potential problem: the @@ -1587,7 +1599,7 @@ successfully applied to image, speech, and text.











                                        -

                                        Dual learning

                                        +

                                        Dual learning

                                        Dual learning is a new learning paradigm, the basic idea of which is @@ -1603,7 +1615,7 @@ image-to-text, and text-to-image.











                                        -

                                        Distributed machine learning

                                        +

                                        Distributed machine learning

                                        Distributed computation will speed up machine learning algorithms, @@ -1614,7 +1626,7 @@ implementing the machine learning algorithms in parallel is required.











                                        -

                                        Meta learning

                                        +

                                        Meta learning

                                        Meta learning is an emerging research direction in machine @@ -1628,7 +1640,7 @@ tasks.











                                        -

                                        The Challenges Facing Machine Learning

                                        +

                                        The Challenges Facing Machine Learning

                                        While there has been much progress in machine learning, there are also challenges. @@ -1653,7 +1665,7 @@ the next ten years will be.











                                        -

                                        Explainable machine learning

                                        +

                                        Explainable machine learning

                                        Machine learning, especially deep learning, evolves rapidly. The @@ -1678,7 +1690,7 @@ problems by logical reasoning. Bayesian Machine Learning is one of the exciting











                                        -

                                        Quantum machine learning

                                        +

                                        Quantum machine learning

                                        Quantum machine learning is an emerging interdisciplinary research @@ -1709,7 +1721,7 @@ computing systems.











                                        -

                                        Quantum machine learning algorithms based on linear algebra

                                        +

                                        Quantum machine learning algorithms based on linear algebra

                                        Many quantum machine learning algorithms are based on variants of @@ -1728,7 +1740,7 @@ input data into a quantum system is as yet unknown for most cases.











                                        -

                                        Quantum reinforcement learning

                                        +

                                        Quantum reinforcement learning

                                        In quantum reinforcement learning, a quantum agent interacts with the @@ -1742,7 +1754,7 @@ superconducting circuits and systems of trapped ions.











                                        -

                                        Quantum deep learning

                                        +

                                        Quantum deep learning

                                        Dedicated quantum information processors, such as quantum annealers @@ -1761,7 +1773,7 @@ result.











                                        -

                                        Social machine learning

                                        +

                                        Social machine learning

                                        Machine learning aims to imitate how humans @@ -1779,7 +1791,7 @@ And much more.











                                        -

                                        The last words?

                                        +

                                        The last words?

                                        Early computer scientist Alan Kay said, The best way to predict the @@ -1791,7 +1803,7 @@ topics. Together, we will not just predict the future, but create it.











                                        -

                                        Best wishes to you all and thanks so much for your heroic efforts this semester

                                        +

                                        Best wishes to you all and thanks so much for your heroic efforts this semester





                                        diff --git a/doc/pub/week48/html/week48.html b/doc/pub/week48/html/week48.html index 62712fe3c..473b9f93d 100644 --- a/doc/pub/week48/html/week48.html +++ b/doc/pub/week48/html/week48.html @@ -96,78 +96,82 @@ div { text-align: justify; text-justify: inter-word; } 2, None, '___sec15'), - ('Summary of course', 2, None, '___sec16'), + ('SVMs and Regression and multiclass classification', + 2, + None, + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec17'), - ('Topics we have covered this year', 2, None, '___sec18'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec19'), - ('Machine learning', 2, None, '___sec20'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec21'), - ('Perspective on Machine Learning', 2, None, '___sec22'), - ('Machine Learning Research', 2, None, '___sec23'), - ('Starting your Machine Learning Project', 2, None, '___sec24'), - ('Choose a Model and Algorithm', 2, None, '___sec25'), - ('Preparing Your Data', 2, None, '___sec26'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec27'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec28'), - ('Resampling', 2, None, '___sec29'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec30'), - ('Additional courses of interest', 2, None, '___sec31'), - ("What's the future like?", 2, None, '___sec32'), - ('Types of Machine Learning, a repetition', 2, None, '___sec33'), - ('Why Boltzmann machines?', 2, None, '___sec34'), - ('Boltzmann Machines', 2, None, '___sec35'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec36'), - ('Boltzmann machines (BM)', 2, None, '___sec37'), - ('A standard BM setup', 2, None, '___sec38'), - ('The structure of the RBM network', 2, None, '___sec39'), - ('The network', 2, None, '___sec40'), - ('Goals', 2, None, '___sec41'), - ('Joint distribution', 2, None, '___sec42'), - ('Network Elements, the energy function', 2, None, '___sec43'), - ('Defining different types of RBMs', 2, None, '___sec44'), - ('More about RBMs', 2, None, '___sec45'), - ('Autoencoders: Overarching view', 2, None, '___sec46'), - ('Bayesian Machine Learning', 2, None, '___sec47'), - ('Reinforcement Learning', 2, None, '___sec48'), - ('Transfer learning', 2, None, '___sec49'), - ('Adversarial learning', 2, None, '___sec50'), - ('Dual learning', 2, None, '___sec51'), - ('Distributed machine learning', 2, None, '___sec52'), - ('Meta learning', 2, None, '___sec53'), - ('The Challenges Facing Machine Learning', 2, None, '___sec54'), - ('Explainable machine learning', 2, None, '___sec55'), - ('Quantum machine learning', 2, None, '___sec56'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec57'), - ('Quantum reinforcement learning', 2, None, '___sec58'), - ('Quantum deep learning', 2, None, '___sec59'), - ('Social machine learning', 2, None, '___sec60'), - ('The last words?', 2, None, '___sec61'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec62')]} + '___sec63')]} end of tocinfo --> @@ -871,18 +875,26 @@ And then we are ready to go.











                                        -

                                        Summary of course

                                        +

                                        SVMs and Regression and multiclass classification

                                        + +

                                        +See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.











                                        -

                                        What? Me worry? No final exam in this course!

                                        +

                                        Summary of course

                                        + +

                                        +









                                        + +

                                        What? Me worry? No final exam in this course!















                                        -

                                        Topics we have covered this year

                                        +

                                        Topics we have covered this year

                                        The course has two central parts @@ -894,7 +906,7 @@ The course has two central parts









                                        -

                                        Statistical analysis and optimization of data

                                        +

                                        Statistical analysis and optimization of data

                                        The following topics be covered @@ -911,7 +923,7 @@ The following topics be covered









                                        -

                                        Machine learning

                                        +

                                        Machine learning

                                        The following topics will be covered @@ -955,7 +967,7 @@ The following topics will be covered









                                        -

                                        Learning outcomes and overarching aims of this course

                                        +

                                        Learning outcomes and overarching aims of this course

                                        The course introduces a variety of central algorithms and methods @@ -981,7 +993,7 @@ ethical conduct is emphasized throughout the course.









                                        -

                                        Perspective on Machine Learning

                                        +

                                        Perspective on Machine Learning

                                        1. Rapidly emerging application area
                                          2. @@ -998,7 +1010,7 @@ Neural Networks, etc.











                                          -

                                          Machine Learning Research

                                          +

                                          Machine Learning Research

                                          Where to find recent results: @@ -1013,7 +1025,7 @@ Where to find recent results:









                                          -

                                          Starting your Machine Learning Project

                                          +

                                          Starting your Machine Learning Project

                                          1. Identify problem type: classification, generation, regression
                                            2. @@ -1025,7 +1037,7 @@ Where to find recent results:









                                            -

                                            Choose a Model and Algorithm

                                            +

                                            Choose a Model and Algorithm

                                            1. Supervised?
                                              2. @@ -1035,7 +1047,7 @@ Where to find recent results:









                                              -

                                              Preparing Your Data

                                              +

                                              Preparing Your Data

                                              1. Shuffle your data
                                                2. @@ -1063,7 +1075,7 @@ Where to find recent results:









                                                -

                                                Which Activation and Weights to Choose in Neural Networks

                                                +

                                                Which Activation and Weights to Choose in Neural Networks

                                                1. RELU? ELU?
                                                  2. @@ -1084,7 +1096,7 @@ Where to find recent results:









                                                  -

                                                  Optimization Methods and Hyperparameters

                                                  +

                                                  Optimization Methods and Hyperparameters

                                                  1. Stochastic gradient descent @@ -1107,7 +1119,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie











                                                    -

                                                    Resampling

                                                    +

                                                    Resampling

                                                    When do we resample? @@ -1120,7 +1132,7 @@ When do we resample?









                                                    -

                                                    Other courses on Data science and Machine Learning at UiO

                                                    +

                                                    Other courses on Data science and Machine Learning at UiO

                                                    The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1138,7 +1150,7 @@ The link here Additional courses of interest +

                                                    Additional courses of interest

                                                    1. STK4051 Computational Statistics
                                                      2. @@ -1147,7 +1159,7 @@ The link here What's the future like? +

                                                      What's the future like?

                                                      Based on multi-layer nonlinear neural networks, deep learning can @@ -1170,7 +1182,7 @@ networks have been proposed, such as









                                                      -

                                                      Types of Machine Learning, a repetition

                                                      +

                                                      Types of Machine Learning, a repetition

                                                      @@ -1200,7 +1212,7 @@ Some of the most common tasks are:











                                                      -

                                                      Why Boltzmann machines?

                                                      +

                                                      Why Boltzmann machines?

                                                      What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1215,7 +1227,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                                                      Boltzmann Machines

                                                      Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1234,7 +1246,7 @@ Why use a generative model rather than the more well known discriminative deep n









                                                      -

                                                      Some similarities and differences from DNNs

                                                      +

                                                      Some similarities and differences from DNNs

                                                      1. Both use gradient-descent based learning procedures for minimizing cost functions
                                                        2. @@ -1247,7 +1259,7 @@ History: The RBM was developed by amongst others                                                         Boltzmann machines (BM) +

                                                        Boltzmann machines (BM)

                                                        @@ -1279,7 +1291,7 @@ the learned distribution.











                                                        -

                                                        A standard BM setup

                                                        +

                                                        A standard BM setup

                                                        @@ -1312,7 +1324,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                        -

                                                        The structure of the RBM network

                                                        +

                                                        The structure of the RBM network





                                                        @@ -1320,7 +1332,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                        -

                                                        The network

                                                        +

                                                        The network

                                                        The network layers: @@ -1332,7 +1344,7 @@ Here we take away all lateral connections between nodes in the visible layer as









                                                        -

                                                        Goals

                                                        +

                                                        Goals

                                                        The goal of the hidden layer is to increase the model's expressive @@ -1353,7 +1365,7 @@ over (integrated out).









                                                        -

                                                        Joint distribution

                                                        +

                                                        Joint distribution

                                                        The restricted Boltzmann machine is described by a Bolztmann distribution @@ -1377,7 +1389,7 @@ It is common to ignore \( T_0 \) by setting it to one.











                                                        -

                                                        Network Elements, the energy function

                                                        +

                                                        Network Elements, the energy function

                                                        The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1405,7 +1417,7 @@ The connection between the nodes in the two layers is given by the weights \( w_











                                                        -

                                                        Defining different types of RBMs

                                                        +

                                                        Defining different types of RBMs

                                                        There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                                                        @@ -1443,7 +1455,7 @@ $$











                                                        -

                                                        More about RBMs

                                                        +

                                                        More about RBMs

                                                        1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                                                          2. @@ -1461,7 +1473,7 @@ Other types of units include:









                                                          -

                                                          Autoencoders: Overarching view

                                                          +

                                                          Autoencoders: Overarching view

                                                          Autoencoders are artificial neural networks capable of learning @@ -1499,7 +1511,7 @@ See also A. Geron's textbook, chapter 15.











                                                          -

                                                          Bayesian Machine Learning

                                                          +

                                                          Bayesian Machine Learning

                                                          This is an important topic if we aim at extracting a probability @@ -1521,7 +1533,7 @@ See also the Reinforcement Learning +

                                                          Reinforcement Learning

                                                          Reinforcement Learning (RL) is one of the most exciting fields of @@ -1555,7 +1567,7 @@ learning. See also A. Geron's textbook, chapter 16.









                                                          -

                                                          Transfer learning

                                                          +

                                                          Transfer learning

                                                          The goal of transfer learning is to transfer the model or knowledge @@ -1573,7 +1585,7 @@ research topic in recent years, with many problems still waiting to be studied.











                                                          -

                                                          Adversarial learning

                                                          +

                                                          Adversarial learning

                                                          The conventional deep generative model has a potential problem: the @@ -1592,7 +1604,7 @@ successfully applied to image, speech, and text.











                                                          -

                                                          Dual learning

                                                          +

                                                          Dual learning

                                                          Dual learning is a new learning paradigm, the basic idea of which is @@ -1608,7 +1620,7 @@ image-to-text, and text-to-image.











                                                          -

                                                          Distributed machine learning

                                                          +

                                                          Distributed machine learning

                                                          Distributed computation will speed up machine learning algorithms, @@ -1619,7 +1631,7 @@ implementing the machine learning algorithms in parallel is required.











                                                          -

                                                          Meta learning

                                                          +

                                                          Meta learning

                                                          Meta learning is an emerging research direction in machine @@ -1633,7 +1645,7 @@ tasks.











                                                          -

                                                          The Challenges Facing Machine Learning

                                                          +

                                                          The Challenges Facing Machine Learning

                                                          While there has been much progress in machine learning, there are also challenges. @@ -1658,7 +1670,7 @@ the next ten years will be.











                                                          -

                                                          Explainable machine learning

                                                          +

                                                          Explainable machine learning

                                                          Machine learning, especially deep learning, evolves rapidly. The @@ -1683,7 +1695,7 @@ problems by logical reasoning. Bayesian Machine Learning is one of the exciting











                                                          -

                                                          Quantum machine learning

                                                          +

                                                          Quantum machine learning

                                                          Quantum machine learning is an emerging interdisciplinary research @@ -1714,7 +1726,7 @@ computing systems.











                                                          -

                                                          Quantum machine learning algorithms based on linear algebra

                                                          +

                                                          Quantum machine learning algorithms based on linear algebra

                                                          Many quantum machine learning algorithms are based on variants of @@ -1733,7 +1745,7 @@ input data into a quantum system is as yet unknown for most cases.











                                                          -

                                                          Quantum reinforcement learning

                                                          +

                                                          Quantum reinforcement learning

                                                          In quantum reinforcement learning, a quantum agent interacts with the @@ -1747,7 +1759,7 @@ superconducting circuits and systems of trapped ions.











                                                          -

                                                          Quantum deep learning

                                                          +

                                                          Quantum deep learning

                                                          Dedicated quantum information processors, such as quantum annealers @@ -1766,7 +1778,7 @@ result.











                                                          -

                                                          Social machine learning

                                                          +

                                                          Social machine learning

                                                          Machine learning aims to imitate how humans @@ -1784,7 +1796,7 @@ And much more.











                                                          -

                                                          The last words?

                                                          +

                                                          The last words?

                                                          Early computer scientist Alan Kay said, The best way to predict the @@ -1796,7 +1808,7 @@ topics. Together, we will not just predict the future, but create it.











                                                          -

                                                          Best wishes to you all and thanks so much for your heroic efforts this semester

                                                          +

                                                          Best wishes to you all and thanks so much for your heroic efforts this semester





                                                          diff --git a/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz b/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz index 4b9dbbba2a37bc5444bbcd3f22a4745b704f7354..5befe539530eb69afc44ddf37cf1ea050218c44c 100644 GIT binary patch delta 53 zcmaDg%jnfCBR2VN4u;!qjcl!KjIC@;t!&J#Y%Hy8tgUQ75%yL#j#f6#RyM9yHtwx# HJSn{ZrHT!} delta 53 zcmaDg%jnfCBR2VN4u(^%jcl!KjIC@;t!&J#Y%Hy8tgUQ75%yL#j#f6#RyM9yHtwx# HJSn{ZqL&S> diff --git a/doc/pub/week48/ipynb/week48.ipynb b/doc/pub/week48/ipynb/week48.ipynb index b1656ffc5..9b7a9299d 100644 --- a/doc/pub/week48/ipynb/week48.ipynb +++ b/doc/pub/week48/ipynb/week48.ipynb @@ -871,7 +871,9 @@ "source": [ "And then we are ready to go.\n", "\n", + "## SVMs and Regression and multiclass classification\n", "\n", + "[See handwritten lectures from lecture November 26.](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesNovember26.pdf). More text will be added here. See also [Hastie et al.](https://www.springer.com/gp/book/9780387848570) section 12.3.\n", "\n", "## Summary of course\n", "\n", diff --git a/doc/src/week48/week48.do.txt b/doc/src/week48/week48.do.txt index 98166a542..af6fef55b 100644 --- a/doc/src/week48/week48.do.txt +++ b/doc/src/week48/week48.do.txt @@ -620,7 +620,10 @@ C \\ And then we are ready to go. +!split +===== SVMs and Regression and multiclass classification ===== +"See handwritten lectures from lecture November 26.":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesNovember26.pdf". More text will be added here. See also "Hastie et al.":"https://www.springer.com/gp/book/9780387848570" section 12.3. !split ===== Summary of course =====