+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({
@@ -287,7 +291,7 @@ MathJax.Hub.Config({
-The course has two central parts - -
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? 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({
-The following topics be covered +The course has two central parts
@@ -298,7 +298,7 @@ The following topics be covered
-The following topics will be covered +The following topics be covered
@@ -325,7 +303,7 @@ The following topics will be covered
-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 -
@@ -307,7 +330,7 @@ ethical conduct is emphasized throughout the 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({
-Where to find recent results: +
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({
+Where to find recent results:
@@ -293,7 +301,7 @@ MathJax.Hub.Config({
@@ -291,7 +298,7 @@ MathJax.Hub.Config({
@@ -309,7 +296,7 @@ MathJax.Hub.Config({
@@ -302,7 +314,7 @@ MathJax.Hub.Config({
-When do we resample? +
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({
-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?
@@ -299,7 +299,7 @@ The link here 40
+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.
@@ -290,7 +304,7 @@ MathJax.Hub.Config({
-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 +
@@ -304,7 +295,7 @@ networks have been proposed, such as
-
-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 -
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({
-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: + +
-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
-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. -
+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. -
+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. +
+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({
-
-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. -
@@ -316,7 +299,7 @@ the learned distribution.
-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
-
+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({
-The network layers: - -
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({
-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:
@@ -302,7 +298,7 @@ over (integrated out).
-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: + +
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({
-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_
-
+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: -$$ -\begin{align} - E(\mathbf{x}, \mathbf{h}) = \sum_i^M \frac{(x_i - a_i)^2}{2\sigma_i^2} - \sum_j^N b_j h_j - \sum_{i,j}^{M,N} \frac{x_i w_{ij} h_j}{\sigma_i^2}. -\tag{4} -\end{align} -$$ -
+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 @@ $$
+
-Other types of units include: +
+RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows: +$$ +\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} +$$ -
+ +
+Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary: +$$ +\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({
-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. +
-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. +
-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? 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({
-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
-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({
-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.
-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.
-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.
-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.
-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.
-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.
-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
-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.
-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.
-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.
-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.
-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.
-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.
-
-
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({
+See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.
The course has two central parts
@@ -886,7 +894,7 @@ The course has two central parts
The following topics be covered
@@ -904,7 +912,7 @@ The following topics be covered
The following topics will be covered
@@ -946,7 +954,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -973,7 +981,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1007,7 +1015,7 @@ Where to find recent results:
When do we resample?
@@ -1123,7 +1131,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1176,7 +1184,7 @@ networks have been proposed, such as
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
+
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
The network layers:
@@ -1340,7 +1348,7 @@ Here we take away all lateral connections between nodes in the visible layer as
The goal of the hidden layer is to increase the model's expressive
@@ -1362,7 +1370,7 @@ over (integrated out).
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.
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_
@@ -1457,7 +1465,7 @@ $$
Autoencoders are artificial neural networks capable of learning
@@ -1515,7 +1523,7 @@ See also A. Geron's textbook, chapter 15.
This is an important topic if we aim at extracting a probability
@@ -1537,7 +1545,7 @@ See also the 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.
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.
The conventional deep generative model has a potential problem: the
@@ -1610,7 +1618,7 @@ successfully applied to image, speech, and text.
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 computation will speed up machine learning algorithms,
@@ -1637,7 +1645,7 @@ implementing the machine learning algorithms in parallel is required.
Meta learning is an emerging research direction in machine
@@ -1651,7 +1659,7 @@ tasks.
While there has been much progress in machine learning, there are also challenges.
@@ -1676,7 +1684,7 @@ the next ten years will be.
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 is an emerging interdisciplinary research
@@ -1732,7 +1740,7 @@ computing systems.
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.
In quantum reinforcement learning, a quantum agent interacts with the
@@ -1765,7 +1773,7 @@ superconducting circuits and systems of trapped ions.
Dedicated quantum information processors, such as quantum annealers
@@ -1784,7 +1792,7 @@ result.
Machine learning aims to imitate how humans
@@ -1802,7 +1810,7 @@ And much more.
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.
+See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.
+
The course has two central parts
@@ -889,7 +901,7 @@ The course has two central parts
The following topics be covered
@@ -906,7 +918,7 @@ The following topics be covered
The following topics will be covered
@@ -950,7 +962,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -976,7 +988,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1008,7 +1020,7 @@ Where to find recent results:
When do we resample?
@@ -1115,7 +1127,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1165,7 +1177,7 @@ networks have been proposed, such as
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
+
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
The network layers:
@@ -1327,7 +1339,7 @@ Here we take away all lateral connections between nodes in the visible layer as
The goal of the hidden layer is to increase the model's expressive
@@ -1348,7 +1360,7 @@ over (integrated out).
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.
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_
@@ -1438,7 +1450,7 @@ $$
Autoencoders are artificial neural networks capable of learning
@@ -1494,7 +1506,7 @@ See also A. Geron's textbook, chapter 15.
This is an important topic if we aim at extracting a probability
@@ -1516,7 +1528,7 @@ See also the 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.
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.
The conventional deep generative model has a potential problem: the
@@ -1587,7 +1599,7 @@ successfully applied to image, speech, and text.
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 computation will speed up machine learning algorithms,
@@ -1614,7 +1626,7 @@ implementing the machine learning algorithms in parallel is required.
Meta learning is an emerging research direction in machine
@@ -1628,7 +1640,7 @@ tasks.
While there has been much progress in machine learning, there are also challenges.
@@ -1653,7 +1665,7 @@ the next ten years will be.
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 is an emerging interdisciplinary research
@@ -1709,7 +1721,7 @@ computing systems.
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.
In quantum reinforcement learning, a quantum agent interacts with the
@@ -1742,7 +1754,7 @@ superconducting circuits and systems of trapped ions.
Dedicated quantum information processors, such as quantum annealers
@@ -1761,7 +1773,7 @@ result.
Machine learning aims to imitate how humans
@@ -1779,7 +1791,7 @@ And much more.
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.
+See handwritten lectures from lecture November 26.. More text will be added here. See also Hastie et al. section 12.3.
+
The course has two central parts
@@ -894,7 +906,7 @@ The course has two central parts
The following topics be covered
@@ -911,7 +923,7 @@ The following topics be covered
The following topics will be covered
@@ -955,7 +967,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -981,7 +993,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1013,7 +1025,7 @@ Where to find recent results:
When do we resample?
@@ -1120,7 +1132,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1170,7 +1182,7 @@ networks have been proposed, such as
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
+
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
Statistical analysis and optimization of data
+Statistical analysis and optimization of data
Machine learning
+Machine learning
Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
Perspective on Machine Learning
+Perspective on Machine Learning
Machine Learning Research
+Machine Learning Research
Starting your Machine Learning Project
+Starting your Machine Learning Project
Choose a Model and Algorithm
+Choose a Model and Algorithm
Preparing Your Data
+Preparing Your Data
Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
Resampling
+Resampling
Other courses on Data science and Machine Learning at UiO
+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
+Types of Machine Learning, a repetition
@@ -1209,7 +1217,7 @@ Some of the most common tasks are:
Why Boltzmann machines?
+Why Boltzmann machines?
Boltzmann Machines
Some similarities and differences from DNNs
+Some similarities and differences from DNNs
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
Goals
+Goals
Joint distribution
+Joint distribution
Network Elements, the energy function
+Network Elements, the energy function
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 \( E(\mathbf{x},\mathbf{h}) \).
More about RBMs
+More about RBMs
Autoencoders: Overarching view
+Autoencoders: Overarching view
Bayesian Machine Learning
+Bayesian Machine Learning
Reinforcement Learning
Transfer learning
+Transfer learning
Adversarial learning
+Adversarial learning
Dual learning
+Dual learning
Distributed machine learning
+Distributed machine learning
Meta learning
+Meta learning
The Challenges Facing Machine Learning
+The Challenges Facing Machine Learning
Explainable machine learning
+Explainable machine learning
Quantum machine learning
+Quantum machine learning
Quantum machine learning algorithms based on linear algebra
+Quantum machine learning algorithms based on linear algebra
Quantum reinforcement learning
+Quantum reinforcement learning
Quantum deep learning
+Quantum deep learning
Social machine learning
+Social machine learning
The last words?
+The last words?
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
+
+
-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
-Statistical analysis and optimization of data
+Statistical analysis and optimization of data
-Machine learning
+Machine learning
-Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
-Perspective on Machine Learning
+Perspective on Machine Learning
-Machine Learning Research
+Machine Learning Research
-Starting your Machine Learning Project
+Starting your Machine Learning Project
-Choose a Model and Algorithm
+Choose a Model and Algorithm
-Preparing Your Data
+Preparing Your Data
-Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
-Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
-Resampling
+Resampling
-Other courses on Data science and Machine Learning at UiO
+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
+Types of Machine Learning, a repetition
@@ -1195,7 +1207,7 @@ Some of the most common tasks are:
-Why Boltzmann machines?
+Why Boltzmann machines?
Boltzmann Machines
-Some similarities and differences from DNNs
+Some similarities and differences from DNNs
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
-Goals
+Goals
-Joint distribution
+Joint distribution
-Network Elements, the energy function
+Network Elements, the energy function
-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 \( E(\mathbf{x},\mathbf{h}) \).
-More about RBMs
+More about RBMs
-Autoencoders: Overarching view
+Autoencoders: Overarching view
-Bayesian Machine Learning
+Bayesian Machine Learning
Reinforcement Learning
-Transfer learning
+Transfer learning
-Adversarial learning
+Adversarial learning
-Dual learning
+Dual learning
-Distributed machine learning
+Distributed machine learning
-Meta learning
+Meta learning
-The Challenges Facing Machine Learning
+The Challenges Facing Machine Learning
-Explainable machine learning
+Explainable machine learning
-Quantum machine learning
+Quantum machine learning
-Quantum machine learning algorithms based on linear algebra
+Quantum machine learning algorithms based on linear algebra
-Quantum reinforcement learning
+Quantum reinforcement learning
-Quantum deep learning
+Quantum deep learning
-Social machine learning
+Social machine learning
-The last words?
+The last words?
-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
+
+
-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
-Statistical analysis and optimization of data
+Statistical analysis and optimization of data
-Machine learning
+Machine learning
-Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
-Perspective on Machine Learning
+Perspective on Machine Learning
-Machine Learning Research
+Machine Learning Research
-Starting your Machine Learning Project
+Starting your Machine Learning Project
-Choose a Model and Algorithm
+Choose a Model and Algorithm
-Preparing Your Data
+Preparing Your Data
-Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
-Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
-Resampling
+Resampling
-Other courses on Data science and Machine Learning at UiO
+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
+Types of Machine Learning, a repetition
@@ -1200,7 +1212,7 @@ Some of the most common tasks are:
-Why Boltzmann machines?
+Why Boltzmann machines?
Boltzmann Machines
-Some similarities and differences from DNNs
+Some similarities and differences from DNNs