From 82b92f55421fab0d45f6c6446c2d4d5a60079c7c Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 26 Aug 2021 09:59:50 +0200 Subject: [PATCH] updating week 35 --- doc/pub/week34/html/._week34-bs000.html | 90 +++--- doc/pub/week34/html/._week34-bs001.html | 90 +++--- doc/pub/week34/html/._week34-bs002.html | 90 +++--- doc/pub/week34/html/._week34-bs003.html | 90 +++--- doc/pub/week34/html/._week34-bs004.html | 90 +++--- doc/pub/week34/html/._week34-bs005.html | 113 ++++---- doc/pub/week34/html/._week34-bs006.html | 126 ++++----- doc/pub/week34/html/._week34-bs007.html | 128 +++++---- doc/pub/week34/html/._week34-bs008.html | 117 ++++---- doc/pub/week34/html/._week34-bs009.html | 120 ++++---- doc/pub/week34/html/._week34-bs010.html | 135 ++++----- doc/pub/week34/html/._week34-bs011.html | 133 ++++----- doc/pub/week34/html/._week34-bs012.html | 126 +++++---- doc/pub/week34/html/._week34-bs013.html | 106 +++---- doc/pub/week34/html/._week34-bs014.html | 117 ++++---- doc/pub/week34/html/._week34-bs015.html | 141 ++++------ doc/pub/week34/html/._week34-bs016.html | 181 +++++------- doc/pub/week34/html/._week34-bs017.html | 172 ++++++++---- doc/pub/week34/html/._week34-bs018.html | 117 ++++---- doc/pub/week34/html/._week34-bs019.html | 104 +++---- doc/pub/week34/html/._week34-bs020.html | 119 ++++---- doc/pub/week34/html/._week34-bs021.html | 133 ++++----- doc/pub/week34/html/._week34-bs022.html | 129 ++++----- doc/pub/week34/html/._week34-bs023.html | 142 +++++----- doc/pub/week34/html/._week34-bs024.html | 151 +++++----- doc/pub/week34/html/._week34-bs025.html | 131 +++++---- doc/pub/week34/html/._week34-bs026.html | 122 ++++---- doc/pub/week34/html/._week34-bs027.html | 132 ++++----- doc/pub/week34/html/._week34-bs028.html | 131 +++++---- doc/pub/week34/html/._week34-bs029.html | 148 ++++------ doc/pub/week34/html/._week34-bs030.html | 152 ++++++---- doc/pub/week34/html/._week34-bs031.html | 116 ++++---- doc/pub/week34/html/._week34-bs032.html | 183 ++++-------- doc/pub/week34/html/._week34-bs033.html | 275 ++++++++----------- doc/pub/week34/html/week34-bs.html | 90 +++--- doc/pub/week34/html/week34-reveal.html | 14 +- doc/pub/week34/html/week34-solarized.html | 15 +- doc/pub/week34/html/week34.html | 15 +- doc/pub/week34/ipynb/ipynb-week34-src.tar.gz | Bin 103349 -> 103349 bytes doc/pub/week34/ipynb/week34.ipynb | 10 +- doc/src/week34/week34.do.txt | 11 +- 41 files changed, 2292 insertions(+), 2313 deletions(-) diff --git a/doc/pub/week34/html/._week34-bs000.html b/doc/pub/week34/html/._week34-bs000.html index 6e80662a5..131104471 100644 --- a/doc/pub/week34/html/._week34-bs000.html +++ b/doc/pub/week34/html/._week34-bs000.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -299,7 +301,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs001.html b/doc/pub/week34/html/._week34-bs001.html index 8b12b4254..1c0dde4f0 100644 --- a/doc/pub/week34/html/._week34-bs001.html +++ b/doc/pub/week34/html/._week34-bs001.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -291,7 +293,7 @@ MathJax.Hub.Config({
  • 10
  • 11
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs002.html b/doc/pub/week34/html/._week34-bs002.html index 1bf8f0c03..d2294ac34 100644 --- a/doc/pub/week34/html/._week34-bs002.html +++ b/doc/pub/week34/html/._week34-bs002.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -294,7 +296,7 @@ Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2.
  • 11
  • 12
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs003.html b/doc/pub/week34/html/._week34-bs003.html index c16a951f4..ddfeefeb4 100644 --- a/doc/pub/week34/html/._week34-bs003.html +++ b/doc/pub/week34/html/._week34-bs003.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -283,7 +285,7 @@ The lectures will be recorded and updated videos will be posted after the lectur
  • 12
  • 13
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs004.html b/doc/pub/week34/html/._week34-bs004.html index cae791f9a..27adaac12 100644 --- a/doc/pub/week34/html/._week34-bs004.html +++ b/doc/pub/week34/html/._week34-bs004.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -295,7 +297,7 @@ MathJax.Hub.Config({
  • 13
  • 14
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs005.html b/doc/pub/week34/html/._week34-bs005.html index 8a7ffd83c..2bdad1c48 100644 --- a/doc/pub/week34/html/._week34-bs005.html +++ b/doc/pub/week34/html/._week34-bs005.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,29 +258,10 @@ MathJax.Hub.Config({ -

    Course Format

    +

    Announcement

    -

    -
    -

    - -

      -
    • Three compulsory projects. Electronic reports only using Canvas to hand in projects and git as version control software and GitHub for repository (or GitLab) of all your material.
      • -
    • Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam. - -
        -
      1. For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project.
        2. -
      2. If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course
        3. -
      3. Poster session where all participants can study and discuss the other proposals.
        4. -
      4. Based on feedback etc, each group finalizes the report and submits for grading.
        5. -
      - -
    • Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and github address of the course.
      • -
    -
    -
    - +NORA AI competetion: See the link here https://www.nora.ai/Competition/image-segmentation.html

    @@ -301,7 +284,7 @@ MathJax.Hub.Config({

  • 14
  • 15
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs006.html b/doc/pub/week34/html/._week34-bs006.html index 817c68715..76b3d005b 100644 --- a/doc/pub/week34/html/._week34-bs006.html +++ b/doc/pub/week34/html/._week34-bs006.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,41 +258,25 @@ MathJax.Hub.Config({ -

    Teachers

    +

    Course Format

    -

    -Teachers : -

      -
    • Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
      • +
    • Three compulsory projects. Electronic reports only using Canvas to hand in projects and git as version control software and GitHub for repository (or GitLab) of all your material.
      • +
    • Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam. -
        -
      • Phone: +47-48257387
        • -
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ470
        • -
      • Office hours: Anytime! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
        • -
      +
        +
      1. For the last project each group/participant submits a proposal or works with suggested (by us) proposals for the project.
        2. +
      2. If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course
        3. +
      3. Poster session where all participants can study and discuss the other proposals.
        4. +
      4. Based on feedback etc, each group finalizes the report and submits for grading.
        5. +
      -
    • Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
      • - -
        -
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ452
        • -
      - -
    • Stian Dysthe Bilek stian.bilek@fys.uio.no
      • - -
        -
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ450
        • -
      - -
    • Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
      • -
    • Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
      • -
    • Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
      • -
    • Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
      • +
    • Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programming languages. All source codes discussed during the lectures can be found at the webpage and github address of the course.
    @@ -318,7 +304,7 @@ MathJax.Hub.Config({
  • 15
  • 16
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs007.html b/doc/pub/week34/html/._week34-bs007.html index 2b4307912..912322dff 100644 --- a/doc/pub/week34/html/._week34-bs007.html +++ b/doc/pub/week34/html/._week34-bs007.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,22 +258,42 @@ MathJax.Hub.Config({ -

    Deadlines for projects (tentative)

    +

    Teachers

    -

      -
    1. Project 1: September 27 (graded with feedback)
      2. -
    2. Project 2: November 1 (graded with feedback)
      3. -
    3. Project 3: December 6 (graded with feedback)
      4. -
    - -Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas. -

    +Teachers : + +

      +
    • Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
      • + +
        +
      • Phone: +47-48257387
        • +
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ470
        • +
      • Office hours: Anytime! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
        • +
      + +
    • Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
      • + +
        +
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ452
        • +
      + +
    • Stian Dysthe Bilek stian.bilek@fys.uio.no
      • + +
        +
      • Office: Department of Physics, University of Oslo, Eastern wing, room FØ450
        • +
      + +
    • Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
      • +
    • Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
      • +
    • Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
      • +
    • Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
      • +
    @@ -299,7 +321,7 @@ Projects are handed in using Canvas. We use Github as repository for code
  • 16
  • 17
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs008.html b/doc/pub/week34/html/._week34-bs008.html index 6e92796fb..6ef355377 100644 --- a/doc/pub/week34/html/._week34-bs008.html +++ b/doc/pub/week34/html/._week34-bs008.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,26 +258,27 @@ MathJax.Hub.Config({ - +

    Deadlines for projects (tentative)

    + +

    +

    +
    +

      -
    1. The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
      2. +
    2. Project 1: October 4 (available September 10) graded with feedback)
      3. +
    3. Project 2: November 8 (available October 8, graded with feedback)
      4. +
    4. Project 3: December 13 (available November 12, graded with feedback)
    -In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below. +Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas. -
      -
    1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.
      2. -
    2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
      3. -
    +

    +

    +
    -Additional textbooks: - -
      -
    1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
      2. -
    2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
      3. -
    +

    diff --git a/doc/pub/week34/html/._week34-bs009.html b/doc/pub/week34/html/._week34-bs009.html index b669e82eb..105e332a5 100644 --- a/doc/pub/week34/html/._week34-bs009.html +++ b/doc/pub/week34/html/._week34-bs009.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,20 +258,26 @@ MathJax.Hub.Config({ -

    Prerequisites

    + -

    -Basic knowledge in programming and mathematics, with an emphasis on -linear algebra. Knowledge of Python or/and C++ as programming -languages is strongly recommended and experience with Jupiter notebook -is recommended. Required courses are the equivalents to the University -of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one -of the corresponding computing and programming courses INF1000/INF1110 -or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities -offer nowadays a basic programming course (often compulsory) where -Python is the recurring programming language. +

      +
    1. The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
      2. +
    + +In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below. + +
      +
    1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.
      2. +
    2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
      3. +
    + +Additional textbooks: + +
      +
    1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
      2. +
    2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
      3. +
    -

    diff --git a/doc/pub/week34/html/._week34-bs010.html b/doc/pub/week34/html/._week34-bs010.html index be858d636..6e3a99738 100644 --- a/doc/pub/week34/html/._week34-bs010.html +++ b/doc/pub/week34/html/._week34-bs010.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,43 +258,18 @@ MathJax.Hub.Config({ -

    Learning outcomes

    +

    Prerequisites

    -

    -
    -

    - -

    -This course aims at giving you insights and knowledge about many of -the central algorithms used in Data Analysis and Machine Learning. -The course is project based and through various numerical 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. Both supervised and unsupervised methods will be covered. The -emphasis is on a frequentist approach, although we will try to link it -with a Bayesian approach as well. You will learn to develop and -structure large codes for studying different cases where Machine -Learning is applied to, get acquainted with computing facilities and -learn to handle large scientific projects. A good scientific and -ethical conduct is emphasized throughout the course. More -specifically, after this course you will - -

      -
    • Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
      • -
    • Be capable of extending the acquired knowledge to other systems and cases;
      • -
    • Have an understanding of central algorithms used in data analysis and machine learning;
      • -
    • Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;
      • -
    • Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
      • -
    • Learn about about decision trees, random forests, bagging and boosting methods;
      • -
    • Learn about support vector machines and kernel transformations;
      • -
    • Reduction of data sets, from PCA to clustering;
      • -
    • Autoencoders and Reinforcement Learning;
      • -
    • Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later) or Julia or other.
      • -
    -
    -
    - +Basic knowledge in programming and mathematics, with an emphasis on +linear algebra. Knowledge of Python or/and C++ as programming +languages is strongly recommended and experience with Jupiter notebook +is recommended. Required courses are the equivalents to the University +of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one +of the corresponding computing and programming courses INF1000/INF1110 +or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities +offer nowadays a basic programming course (often compulsory) where +Python is the recurring programming language.

    @@ -320,7 +297,7 @@ specifically, after this course you will

  • 19
  • 20
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs011.html b/doc/pub/week34/html/._week34-bs011.html index b10db317a..67d032aa5 100644 --- a/doc/pub/week34/html/._week34-bs011.html +++ b/doc/pub/week34/html/._week34-bs011.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,17 +258,7 @@ MathJax.Hub.Config({ -

    Topics covered in this course: Statistical analysis and optimization of data

    - -

    -The course has two central parts - -

      -
    1. Statistical analysis and optimization of data
      2. -
    2. Machine learning
      3. -
    - -These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms +

    Learning outcomes

    @@ -274,16 +266,31 @@ These topics will be scattered thorughout the course and may not necessarily be

    -We plan to cover the following topics: +This course aims at giving you insights and knowledge about many of +the central algorithms used in Data Analysis and Machine Learning. +The course is project based and through various numerical 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. Both supervised and unsupervised methods will be covered. The +emphasis is on a frequentist approach, although we will try to link it +with a Bayesian approach as well. You will learn to develop and +structure large codes for studying different cases where Machine +Learning is applied to, get acquainted with computing facilities and +learn to handle large scientific projects. A good scientific and +ethical conduct is emphasized throughout the course. More +specifically, after this course you will

    @@ -315,7 +322,7 @@ We plan to cover the following topics:
  • 20
  • 21
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs012.html b/doc/pub/week34/html/._week34-bs012.html index 46da91b57..79e711055 100644 --- a/doc/pub/week34/html/._week34-bs012.html +++ b/doc/pub/week34/html/._week34-bs012.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,27 +258,35 @@ MathJax.Hub.Config({ -

    Topics covered in this course: Machine Learning

    +

    Topics covered in this course: Statistical analysis and optimization of data

    + +

    +The course has two central parts + +

      +
    1. Statistical analysis and optimization of data
      2. +
    2. Machine learning
      3. +
    + +These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms

    -The following topics will be covered - -

      -
    • Linear Regression and Logistic Regression;
      • -
    • Neural networks and deep learning, including convolutional and recurrent neural networks
      • -
    • Decisions trees, Random Forests, Bagging and Boosting
      • -
    • Support vector machines
      • -
    • Bayesian linear and logistic regression
      • -
    • Boltzmann Machines
      • -
    • Unsupervised learning Dimensionality reduction, from PCA to clustering
      • -
    - -Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.

    +We plan to cover the following topics: + +

      +
    • Basic concepts, expectation values, variance, covariance, correlation functions and errors;
      • +
    • Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
      • +
    • Central elements of Bayesian statistics and modeling;
      • +
    • Gradient methods for data optimization,
      • +
    • Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
      • +
    • Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
      • +
    • Principal Component Analysis (PCA) and its mathematical foundation
      • +
    @@ -307,7 +317,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
  • 21
  • 22
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs013.html b/doc/pub/week34/html/._week34-bs013.html index b370dad31..c6f45106c 100644 --- a/doc/pub/week34/html/._week34-bs013.html +++ b/doc/pub/week34/html/._week34-bs013.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,17 +258,27 @@ MathJax.Hub.Config({ - +

    Topics covered in this course: Machine Learning

    +The following topics will be covered

      -
    • GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session
      • -
    • Anaconda and other Python environments, see intro slides and links to programming resources at https://computationalscienceuio.github.io/RefreshProgrammingSkills/intro.html
      • +
    • Linear Regression and Logistic Regression;
      • +
    • Neural networks and deep learning, including convolutional and recurrent neural networks
      • +
    • Decisions trees, Random Forests, Bagging and Boosting
      • +
    • Support vector machines
      • +
    • Bayesian linear and logistic regression
      • +
    • Boltzmann Machines
      • +
    • Unsupervised learning Dimensionality reduction, from PCA to clustering
    + +Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics. + +

    @@ -297,7 +309,7 @@ MathJax.Hub.Config({
  • 22
  • 23
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs014.html b/doc/pub/week34/html/._week34-bs014.html index a1a952d12..56fb3e71a 100644 --- a/doc/pub/week34/html/._week34-bs014.html +++ b/doc/pub/week34/html/._week34-bs014.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,25 +258,22 @@ MathJax.Hub.Config({ -

    Other courses on Data science and Machine Learning at UiO

    +

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

    +
    +

    -

      -
    1. STK2100 Machine learning and statistical methods for prediction and classification.
      2. -
    2. IN3050/4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      3. -
    3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
      4. -
    4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
      5. -
    5. STK-IN4300 Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
      6. -
    6. INF4490 Biologically Inspired Computing. An introduction to self-adapting methods also called artificial intelligence or machine learning.
      7. -
    7. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
      8. -
    8. IN5400/INF5860 Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
      9. -
    9. TEK5040 Deep learning for autonomous systems. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
      10. -
    10. STK4051 Computational Statistics
      11. -
    11. STK4021 Applied Bayesian Analysis and Numerical Methods
      12. -
    + +
    +
    + +

    diff --git a/doc/pub/week34/html/._week34-bs015.html b/doc/pub/week34/html/._week34-bs015.html index 8dd9b0a83..1c96fd101 100644 --- a/doc/pub/week34/html/._week34-bs015.html +++ b/doc/pub/week34/html/._week34-bs015.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,46 +258,25 @@ MathJax.Hub.Config({ -

    Introduction

    +

    Other courses on Data science and Machine Learning at UiO

    -Our emphasis throughout this series of lectures -is on understanding the mathematical aspects of -different algorithms used in the fields of data analysis and machine learning. +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. -

    -However, where possible we will emphasize the -importance of using available software. We start thus with a hands-on -and top-down approach to machine learning. The aim is thus to start with -relevant data or data we have produced -and use these to introduce statistical data analysis -concepts and machine learning algorithms before we delve into the -algorithms themselves. The examples we will use in the beginning, start with simple -polynomials with random noise added. We will use the Python -software package Scikit-Learn and -introduce various machine learning algorithms to make fits of -the data and predictions. We move thereafter to more interesting -cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). -These are examples where we can easily set up the data and -then use machine learning algorithms included in for example -Scikit-Learn. +

      +
    1. STK2100 Machine learning and statistical methods for prediction and classification.
      2. +
    2. IN3050/4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      3. +
    3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
      4. +
    4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
      5. +
    5. STK-IN4300 Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
      6. +
    6. INF4490 Biologically Inspired Computing. An introduction to self-adapting methods also called artificial intelligence or machine learning.
      7. +
    7. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
      8. +
    8. IN5400/INF5860 Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
      9. +
    9. TEK5040 Deep learning for autonomous systems. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
      10. +
    10. STK4051 Computational Statistics
      11. +
    11. STK4021 Applied Bayesian Analysis and Numerical Methods
      12. +
    -

    -These examples will serve us the purpose of getting -started. Furthermore, they allow us to catch more than two birds with -a stone. They will allow us to bring in some programming specific -topics and tools as well as showing the power of various Python -libraries for machine learning and statistical data analysis. - -

    -Here, we will mainly focus on two -specific Python packages for Machine Learning, Scikit-Learn and -Tensorflow (see below for links etc). Moreover, the examples we -introduce will serve as inputs to many of our discussions later, as -well as allowing you to set up models and produce your own data and -get started with programming. - -

    diff --git a/doc/pub/week34/html/._week34-bs016.html b/doc/pub/week34/html/._week34-bs016.html index c5f908917..372cffeba 100644 --- a/doc/pub/week34/html/._week34-bs016.html +++ b/doc/pub/week34/html/._week34-bs016.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,75 +258,44 @@ MathJax.Hub.Config({ -

    What is Machine Learning?

    +

    Introduction

    -Statistics, data science and machine learning form important fields of -research in modern science. They describe how to learn and make -predictions from data, as well as allowing us to extract important -correlations about physical process and the underlying laws of motion -in large data sets. The latter, big data sets, appear frequently in -essentially all disciplines, from the traditional Science, Technology, -Mathematics and Engineering fields to Life Science, Law, education -research, the Humanities and the Social Sciences. +Our emphasis throughout this series of lectures +is on understanding the mathematical aspects of +different algorithms used in the fields of data analysis and machine learning.

    -It has become more -and more common to see research projects on big data in for example -the Social Sciences where extracting patterns from complicated survey -data is one of many research directions. Having a solid grasp of data -analysis and machine learning is thus becoming central to scientific -computing in many fields, and competences and skills within the fields -of machine learning and scientific computing are nowadays strongly -requested by many potential employers. The latter cannot be -overstated, familiarity with machine learning has almost become a -prerequisite for many of the most exciting employment opportunities, -whether they are in bioinformatics, life science, physics or finance, -in the private or the public sector. This author has had several -students or met students who have been hired recently based on their -skills and competences in scientific computing and data science, often -with marginal knowledge of machine learning. +However, where possible we will emphasize the +importance of using available software. We start thus with a hands-on +and top-down approach to machine learning. The aim is thus to start with +relevant data or data we have produced +and use these to introduce statistical data analysis +concepts and machine learning algorithms before we delve into the +algorithms themselves. The examples we will use in the beginning, start with simple +polynomials with random noise added. We will use the Python +software package Scikit-Learn and +introduce various machine learning algorithms to make fits of +the data and predictions. We move thereafter to more interesting +cases such as data from say experiments (below we will look at experimental nuclear binding energies as an example). +These are examples where we can easily set up the data and +then use machine learning algorithms included in for example +Scikit-Learn.

    -Machine learning is a subfield of computer science, and is closely -related to computational statistics. It evolved from the study of -pattern recognition in artificial intelligence (AI) research, and has -made contributions to AI tasks like computer vision, natural language -processing and speech recognition. Many of the methods we will study are also -strongly rooted in basic mathematics and physics research. +These examples will serve us the purpose of getting +started. Furthermore, they allow us to catch more than two birds with +a stone. They will allow us to bring in some programming specific +topics and tools as well as showing the power of various Python +libraries for machine learning and statistical data analysis.

    -Ideally, machine learning represents the science of giving computers -the ability to learn without being explicitly programmed. The idea is -that there exist generic algorithms which can be used to find patterns -in a broad class of data sets without having to write code -specifically for each problem. The algorithm will build its own logic -based on the data. You should however always keep in mind that -machines and algorithms are to a large extent developed by humans. The -insights and knowledge we have about a specific system, play a central -role when we develop a specific machine learning algorithm. - -

    -Machine learning is an extremely rich field, in spite of its young -age. The increases we have seen during the last three decades in -computational capabilities have been followed by developments of -methods and techniques for analyzing and handling large date sets, -relying heavily on statistics, computer science and mathematics. The -field is rather new and developing rapidly. Popular software packages -written in Python for machine learning like -Scikit-learn, -Tensorflow, -PyTorch and Keras, all -freely available at their respective GitHub sites, encompass -communities of developers in the thousands or more. And the number of -code developers and contributors keeps increasing. Not all the -algorithms and methods can be given a rigorous mathematical -justification, opening up thereby large rooms for experimenting and -trial and error and thereby exciting new developments. However, a -solid command of linear algebra, multivariate theory, probability -theory, statistical data analysis, understanding errors and Monte -Carlo methods are central elements in a proper understanding of many -of algorithms and methods we will discuss. +Here, we will mainly focus on two +specific Python packages for Machine Learning, Scikit-Learn and +Tensorflow (see below for links etc). Moreover, the examples we +introduce will serve as inputs to many of our discussions later, as +well as allowing you to set up models and produce your own data and +get started with programming.

    @@ -352,7 +323,7 @@ of algorithms and methods we will discuss.

  • 25
  • 26
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs017.html b/doc/pub/week34/html/._week34-bs017.html index 691676e83..962570f6e 100644 --- a/doc/pub/week34/html/._week34-bs017.html +++ b/doc/pub/week34/html/._week34-bs017.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,29 +258,77 @@ MathJax.Hub.Config({ -

    Types of Machine Learning

    +

    What is Machine Learning?

    -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 -behavioral psychology, where learning is achieved by trial-and-error, -solely from rewards and punishment. +Statistics, data science and machine learning form important fields of +research in modern science. They describe how to learn and make +predictions from data, as well as allowing us to extract important +correlations about physical process and the underlying laws of motion +in large data sets. The latter, big data sets, appear frequently in +essentially all disciplines, from the traditional Science, Technology, +Mathematics and Engineering fields to Life Science, Law, education +research, the Humanities and the Social Sciences.

    -Another way to categorize machine learning tasks is to consider the -desired output of a system. Some of the most common tasks are: +It has become more +and more common to see research projects on big data in for example +the Social Sciences where extracting patterns from complicated survey +data is one of many research directions. Having a solid grasp of data +analysis and machine learning is thus becoming central to scientific +computing in many fields, and competences and skills within the fields +of machine learning and scientific computing are nowadays strongly +requested by many potential employers. The latter cannot be +overstated, familiarity with machine learning has almost become a +prerequisite for many of the most exciting employment opportunities, +whether they are in bioinformatics, life science, physics or finance, +in the private or the public sector. This author has had several +students or met students who have been hired recently based on their +skills and competences in scientific computing and data science, often +with marginal knowledge of machine learning. -

    +

    +Machine learning is a subfield of computer science, and is closely +related to computational statistics. It evolved from the study of +pattern recognition in artificial intelligence (AI) research, and has +made contributions to AI tasks like computer vision, natural language +processing and speech recognition. Many of the methods we will study are also +strongly rooted in basic mathematics and physics research. +

    +Ideally, machine learning represents the science of giving computers +the ability to learn without being explicitly programmed. The idea is +that there exist generic algorithms which can be used to find patterns +in a broad class of data sets without having to write code +specifically for each problem. The algorithm will build its own logic +based on the data. You should however always keep in mind that +machines and algorithms are to a large extent developed by humans. The +insights and knowledge we have about a specific system, play a central +role when we develop a specific machine learning algorithm. + +

    +Machine learning is an extremely rich field, in spite of its young +age. The increases we have seen during the last three decades in +computational capabilities have been followed by developments of +methods and techniques for analyzing and handling large date sets, +relying heavily on statistics, computer science and mathematics. The +field is rather new and developing rapidly. Popular software packages +written in Python for machine learning like +Scikit-learn, +Tensorflow, +PyTorch and Keras, all +freely available at their respective GitHub sites, encompass +communities of developers in the thousands or more. And the number of +code developers and contributors keeps increasing. Not all the +algorithms and methods can be given a rigorous mathematical +justification, opening up thereby large rooms for experimenting and +trial and error and thereby exciting new developments. However, a +solid command of linear algebra, multivariate theory, probability +theory, statistical data analysis, understanding errors and Monte +Carlo methods are central elements in a proper understanding of many +of algorithms and methods we will discuss. + +

    diff --git a/doc/pub/week34/html/._week34-bs018.html b/doc/pub/week34/html/._week34-bs018.html index 7fb7bf762..78b5cfc54 100644 --- a/doc/pub/week34/html/._week34-bs018.html +++ b/doc/pub/week34/html/._week34-bs018.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,22 +258,29 @@ MathJax.Hub.Config({ -

    Essential elements of ML

    +

    Types of Machine Learning

    -The methods we cover have three main topics in common, irrespective of -whether we deal with supervised or unsupervised learning. - +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 +behavioral psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment. + +

    +Another way to categorize machine learning tasks is to consider the +desired output of a system. Some of the most common tasks are:

    - - -

    diff --git a/doc/pub/week34/html/._week34-bs019.html b/doc/pub/week34/html/._week34-bs019.html index d2b4c5539..3a3a4de69 100644 --- a/doc/pub/week34/html/._week34-bs019.html +++ b/doc/pub/week34/html/._week34-bs019.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,10 +258,20 @@ MathJax.Hub.Config({ -

    An optimization/minimization problem

    +

    Essential elements of ML

    -At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called gradient methods. +The methods we cover have three main topics in common, irrespective of +whether we deal with supervised or unsupervised learning. + + +

    + +

    @@ -287,7 +299,7 @@ At the heart of basically all Machine Learning algorithms we will encounter so-c

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    • -
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  • »
    • diff --git a/doc/pub/week34/html/._week34-bs020.html b/doc/pub/week34/html/._week34-bs020.html index 121fa81f8..8f33a24f6 100644 --- a/doc/pub/week34/html/._week34-bs020.html +++ b/doc/pub/week34/html/._week34-bs020.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,35 +258,10 @@ MathJax.Hub.Config({ -

    A Frequentist approach to data analysis

    +

    An optimization/minimization problem

    -When you hear phrases like predictions and estimations and -correlations and causations, what do you think of? May be you think -of the difference between classifying new data points and generating -new data points. -Or perhaps you consider that correlations represent some kind of symmetric statements like -if \( A \) is correlated with \( B \), then \( B \) is correlated with -\( A \). Causation on the other hand is directional, that is if \( A \) causes \( B \), \( B \) does not -necessarily cause \( A \). - -

    -These concepts are in some sense the difference between machine -learning and statistics. In machine learning and prediction based -tasks, we are often interested in developing algorithms that are -capable of learning patterns from given data in an automated fashion, -and then using these learned patterns to make predictions or -assessments of newly given data. In many cases, our primary concern -is the quality of the predictions or assessments, and we are less -concerned about the underlying patterns that were learned in order -to make these predictions. - -

    -In machine learning we normally use a so-called frequentist approach, -where the aim is to make predictions and find correlations. We focus -less on for example extracting a probability distribution function (PDF). The PDF can be -used in turn to make estimations and find causations such as given \( A \) -what is the likelihood of finding \( B \). +At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called gradient methods.

    @@ -312,7 +289,7 @@ what is the likelihood of finding \( B \).

  • 29
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  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs021.html b/doc/pub/week34/html/._week34-bs021.html index e379dc334..fb2225eed 100644 --- a/doc/pub/week34/html/._week34-bs021.html +++ b/doc/pub/week34/html/._week34-bs021.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,32 +258,35 @@ MathJax.Hub.Config({ -

    What is a good model?

    +

    A Frequentist approach to data analysis

    -In science and engineering we often end up in situations where we want to infer (or learn) a -quantitative model \( M \) for a given set of sample points \( \boldsymbol{X} \in [x_1, x_2,\dots x_N] \). +When you hear phrases like predictions and estimations and +correlations and causations, what do you think of? May be you think +of the difference between classifying new data points and generating +new data points. +Or perhaps you consider that correlations represent some kind of symmetric statements like +if \( A \) is correlated with \( B \), then \( B \) is correlated with +\( A \). Causation on the other hand is directional, that is if \( A \) causes \( B \), \( B \) does not +necessarily cause \( A \).

    -As we will see repeatedely in these lectures, we could try to fit these data points to a model given by a -straight line, or if we wish to be more sophisticated to a more complex -function. +These concepts are in some sense the difference between machine +learning and statistics. In machine learning and prediction based +tasks, we are often interested in developing algorithms that are +capable of learning patterns from given data in an automated fashion, +and then using these learned patterns to make predictions or +assessments of newly given data. In many cases, our primary concern +is the quality of the predictions or assessments, and we are less +concerned about the underlying patterns that were learned in order +to make these predictions.

    -The reason for inferring such a model is that it -serves many useful purposes. On the one hand, the model can reveal information -encoded in the data or underlying mechanisms from which the data were generated. For instance, we could discover important -corelations that relate interesting physics interpretations. - -

    -In addition, it can simplify the representation of the given data set and help -us in making predictions about future data samples. - -

    -A first important consideration to keep in mind is that inferring the correct model -for a given data set is an elusive, if not impossible, task. The fundamental difficulty -is that if we are not specific about what we mean by a correct model, there -could easily be many different models that fit the given data set equally well. +In machine learning we normally use a so-called frequentist approach, +where the aim is to make predictions and find correlations. We focus +less on for example extracting a probability distribution function (PDF). The PDF can be +used in turn to make estimations and find causations such as given \( A \) +what is the likelihood of finding \( B \).

    @@ -309,7 +314,7 @@ could easily be many different models that fit the given data set equally we

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  • »
    • diff --git a/doc/pub/week34/html/._week34-bs022.html b/doc/pub/week34/html/._week34-bs022.html index 2cdbac904..e39c57a0d 100644 --- a/doc/pub/week34/html/._week34-bs022.html +++ b/doc/pub/week34/html/._week34-bs022.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,31 +258,32 @@ MathJax.Hub.Config({ -

    What is a good model? Can we define it?

    +

    What is a good model?

    -The central question is this: what leads us to say that a model is correct or -optimal for a given data set? To make the model inference problem well posed, i.e., -to guarantee that there is a unique optimal model for the given data, we need to -impose additional assumptions or restrictions on the class of models considered. To -this end, we should not be looking for just any model that can describe the data. -Instead, we should look for a model \( M \) that is the best among a restricted class -of models. In addition, to make the model inference problem computationally -tractable, we need to specify how restricted the class of models needs to be. A -common strategy is to start -with the simplest possible class of models that is just necessary to describe the data -or solve the problem at hand. More precisely, the model class should be rich enough -to contain at least one model that can fit the data to a desired accuracy and yet be -restricted enough that it is relatively simple to find the best model for the given data. +In science and engineering we often end up in situations where we want to infer (or learn) a +quantitative model \( M \) for a given set of sample points \( \boldsymbol{X} \in [x_1, x_2,\dots x_N] \).

    -Thus, the most popular strategy is to start from the -simplest class of models and increase the complexity of the models only when the -simpler models become inadequate. For instance, if we work with a regression problem to fit a set of sample points, one -may first try the simplest class of models, namely linear models, followed obviously by more complex models. +As we will see repeatedely in these lectures, we could try to fit these data points to a model given by a +straight line, or if we wish to be more sophisticated to a more complex +function.

    -How to evaluate which model fits best the data is something we will come back to over and over again in these sets of lectures. +The reason for inferring such a model is that it +serves many useful purposes. On the one hand, the model can reveal information +encoded in the data or underlying mechanisms from which the data were generated. For instance, we could discover important +corelations that relate interesting physics interpretations. + +

    +In addition, it can simplify the representation of the given data set and help +us in making predictions about future data samples. + +

    +A first important consideration to keep in mind is that inferring the correct model +for a given data set is an elusive, if not impossible, task. The fundamental difficulty +is that if we are not specific about what we mean by a correct model, there +could easily be many different models that fit the given data set equally well.

    @@ -308,7 +311,7 @@ How to evaluate which model fits best the data is something we will come back to

  • 31
  • 32
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs023.html b/doc/pub/week34/html/._week34-bs023.html index b3d7af6f7..df4885f59 100644 --- a/doc/pub/week34/html/._week34-bs023.html +++ b/doc/pub/week34/html/._week34-bs023.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,45 +258,31 @@ MathJax.Hub.Config({ -

    Software and needed installations

    +

    What is a good model? Can we define it?

    -We will make extensive use of Python as programming language and its -myriad of available libraries. You will find -Jupyter notebooks invaluable in your work. You can run R -codes in the Jupyter/IPython notebooks, with the immediate benefit of -visualizing your data. You can also use compiled languages like C++, -Rust, Julia, Fortran etc if you prefer. The focus in these lectures will be -on Python. +The central question is this: what leads us to say that a model is correct or +optimal for a given data set? To make the model inference problem well posed, i.e., +to guarantee that there is a unique optimal model for the given data, we need to +impose additional assumptions or restrictions on the class of models considered. To +this end, we should not be looking for just any model that can describe the data. +Instead, we should look for a model \( M \) that is the best among a restricted class +of models. In addition, to make the model inference problem computationally +tractable, we need to specify how restricted the class of models needs to be. A +common strategy is to start +with the simplest possible class of models that is just necessary to describe the data +or solve the problem at hand. More precisely, the model class should be rich enough +to contain at least one model that can fit the data to a desired accuracy and yet be +restricted enough that it is relatively simple to find the best model for the given data.

    -If you have Python installed (we strongly recommend Python3) and you feel -pretty familiar with installing different packages, we recommend that -you install the following Python packages via pip as - -

      -
    1. pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow
      2. -
    - -For Python3, replace pip with pip3. +Thus, the most popular strategy is to start from the +simplest class of models and increase the complexity of the models only when the +simpler models become inadequate. For instance, if we work with a regression problem to fit a set of sample points, one +may first try the simplest class of models, namely linear models, followed obviously by more complex models.

    -For OSX users we recommend, after having installed Xcode, to -install brew. Brew allows for a seamless installation of additional -software via for example - -

      -
    1. brew install python3
      2. -
    - -For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution, -you can use pip as well and simply install Python as - -
      -
    1. sudo apt-get install python3 (or python for pyhton2.7)
      2. -
    - -etc etc. +How to evaluate which model fits best the data is something we will come back to over and over again in these sets of lectures.

    @@ -322,7 +310,7 @@ etc etc.

  • 32
  • 33
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs024.html b/doc/pub/week34/html/._week34-bs024.html index b8c30fa3f..a352349cd 100644 --- a/doc/pub/week34/html/._week34-bs024.html +++ b/doc/pub/week34/html/._week34-bs024.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,36 +258,45 @@ MathJax.Hub.Config({ -

    Python installers

    +

    Software and needed installations

    -If you don't want to perform these operations separately and venture -into the hassle of exploring how to set up dependencies and paths, we -recommend two widely used distrubutions which set up all relevant -dependencies for Python, namely - -

    - -which is an open source -distribution of the Python and R programming languages for large-scale -data processing, predictive analytics, and scientific computing, that -aims to simplify package management and deployment. Package versions -are managed by the package management system conda. - - - -is a Python -distribution for scientific and analytic computing distribution and -analysis environment, available for free and under a commercial -license. +We will make extensive use of Python as programming language and its +myriad of available libraries. You will find +Jupyter notebooks invaluable in your work. You can run R +codes in the Jupyter/IPython notebooks, with the immediate benefit of +visualizing your data. You can also use compiled languages like C++, +Rust, Julia, Fortran etc if you prefer. The focus in these lectures will be +on Python.

    -Furthermore, Google's Colab is a free Jupyter notebook environment that requires -no setup and runs entirely in the cloud. Try it out! +If you have Python installed (we strongly recommend Python3) and you feel +pretty familiar with installing different packages, we recommend that +you install the following Python packages via pip as + +

      +
    1. pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow
      2. +
    + +For Python3, replace pip with pip3. + +

    +For OSX users we recommend, after having installed Xcode, to +install brew. Brew allows for a seamless installation of additional +software via for example + +

      +
    1. brew install python3
      2. +
    + +For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution, +you can use pip as well and simply install Python as + +
      +
    1. sudo apt-get install python3 (or python for pyhton2.7)
      2. +
    + +etc etc.

    @@ -313,7 +324,7 @@ no setup and runs entirely in the cloud. Try it out!

  • 33
  • 34
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs025.html b/doc/pub/week34/html/._week34-bs025.html index a7983d8b6..e99d5a594 100644 --- a/doc/pub/week34/html/._week34-bs025.html +++ b/doc/pub/week34/html/._week34-bs025.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,23 +258,38 @@ MathJax.Hub.Config({ -

    Useful Python libraries

    -Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there) +

    Python installers

    + +

    +If you don't want to perform these operations separately and venture +into the hassle of exploring how to set up dependencies and paths, we +recommend two widely used distrubutions which set up all relevant +dependencies for Python, namely

    +which is an open source +distribution of the Python and R programming languages for large-scale +data processing, predictive analytics, and scientific computing, that +aims to simplify package management and deployment. Package versions +are managed by the package management system conda. + + + +is a Python +distribution for scientific and analytic computing distribution and +analysis environment, available for free and under a commercial +license. + +

    +Furthermore, Google's Colab is a free Jupyter notebook environment that requires +no setup and runs entirely in the cloud. Try it out! + +

    diff --git a/doc/pub/week34/html/._week34-bs026.html b/doc/pub/week34/html/._week34-bs026.html index a33e4abe7..d3f594f75 100644 --- a/doc/pub/week34/html/._week34-bs026.html +++ b/doc/pub/week34/html/._week34-bs026.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,25 +258,23 @@ MathJax.Hub.Config({ -

    Installing R, C++, cython or Julia

    +

    Useful Python libraries

    +Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there) -

    -You will also find it convenient to utilize R. We will mainly -use Python during our lectures and in various projects and exercises. -Those of you -already familiar with R should feel free to continue using R, keeping -however an eye on the parallel Python set ups. Similarly, if you are a -Python afecionado, feel free to explore R as well. Jupyter/Ipython -notebook allows you to run R codes interactively in your -browser. The software library R is really tailored for statistical data analysis -and allows for an easy usage of the tools and algorithms we will discuss in these -lectures. +

    -

    -To install R with Jupyter notebook -follow the link here - -

    diff --git a/doc/pub/week34/html/._week34-bs027.html b/doc/pub/week34/html/._week34-bs027.html index 790fead42..bed5cbc81 100644 --- a/doc/pub/week34/html/._week34-bs027.html +++ b/doc/pub/week34/html/._week34-bs027.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,39 +258,23 @@ MathJax.Hub.Config({ -

    Installing R, C++, cython, Numba etc

    +

    Installing R, C++, cython or Julia

    -For the C++ aficionados, Jupyter/IPython notebook allows you also to -install C++ and run codes written in this language interactively in -the browser. Since we will emphasize writing many of the algorithms -yourself, you can thus opt for either Python or C++ (or Fortran or other compiled languages) as programming -languages. +You will also find it convenient to utilize R. We will mainly +use Python during our lectures and in various projects and exercises. +Those of you +already familiar with R should feel free to continue using R, keeping +however an eye on the parallel Python set ups. Similarly, if you are a +Python afecionado, feel free to explore R as well. Jupyter/Ipython +notebook allows you to run R codes interactively in your +browser. The software library R is really tailored for statistical data analysis +and allows for an easy usage of the tools and algorithms we will discuss in these +lectures.

    -To add more entropy, cython can also be used when running your -notebooks. It means that Python with the jupyter notebook -setup allows you to integrate widely popular softwares and tools for -scientific computing. Similarly, the -Numba Python package delivers increased performance -capabilities with minimal rewrites of your codes. With its -versatility, including symbolic operations, Python offers a unique -computational environment. Your jupyter notebook can easily be -converted into a nicely rendered PDF file or a Latex file for -further processing. For example, convert to latex as - -

    - - -

    pycod jupyter nbconvert filename.ipynb --to latex 
-
    -

    -And to add more versatility, the Python package SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) and is entirely written in Python. - -

    -Finally, if you wish to use the light mark-up language -doconce you can convert a standard ascii text file into various HTML -formats, ipython notebooks, latex files, pdf files etc with minimal edits. These lectures were generated using doconce. +To install R with Jupyter notebook +follow the link here

    @@ -316,7 +302,7 @@ formats, ipython notebooks, latex files, pdf files etc with minimal edits. These

  • 36
  • 37
  • ...
    • -
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs028.html b/doc/pub/week34/html/._week34-bs028.html index f6520ace2..630e43ffe 100644 --- a/doc/pub/week34/html/._week34-bs028.html +++ b/doc/pub/week34/html/._week34-bs028.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,20 +258,41 @@ MathJax.Hub.Config({ -

    Numpy examples and Important Matrix and vector handling packages

    +

    Installing R, C++, cython, Numba etc

    -There are several central software libraries for linear algebra and eigenvalue problems. Several of the more -popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used -software package LAPACK, which follows two other popular packages -developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. +For the C++ aficionados, Jupyter/IPython notebook allows you also to +install C++ and run codes written in this language interactively in +the browser. Since we will emphasize writing many of the algorithms +yourself, you can thus opt for either Python or C++ (or Fortran or other compiled languages) as programming +languages. -

    +

    +To add more entropy, cython can also be used when running your +notebooks. It means that Python with the jupyter notebook +setup allows you to integrate widely popular softwares and tools for +scientific computing. Similarly, the +Numba Python package delivers increased performance +capabilities with minimal rewrites of your codes. With its +versatility, including symbolic operations, Python offers a unique +computational environment. Your jupyter notebook can easily be +converted into a nicely rendered PDF file or a Latex file for +further processing. For example, convert to latex as +

    + + +

    pycod jupyter nbconvert filename.ipynb --to latex 
+
    +

    +And to add more versatility, the Python package SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) and is entirely written in Python. + +

    +Finally, if you wish to use the light mark-up language +doconce you can convert a standard ascii text file into various HTML +formats, ipython notebooks, latex files, pdf files etc with minimal edits. These lectures were generated using doconce. + +

    diff --git a/doc/pub/week34/html/._week34-bs029.html b/doc/pub/week34/html/._week34-bs029.html index 69dd84cf2..e67cf1c96 100644 --- a/doc/pub/week34/html/._week34-bs029.html +++ b/doc/pub/week34/html/._week34-bs029.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,58 +258,20 @@ MathJax.Hub.Config({ -

    Basic Matrix Features

    +

    Numpy examples and Important Matrix and vector handling packages

    -

    -
    -

    -$$ - \mathbf{A} = - \begin{bmatrix} a_{11} & a_{12} & a_{13} & a_{14} \\ - a_{21} & a_{22} & a_{23} & a_{24} \\ - a_{31} & a_{32} & a_{33} & a_{34} \\ - a_{41} & a_{42} & a_{43} & a_{44} - \end{bmatrix}\qquad -\mathbf{I} = - \begin{bmatrix} 1 & 0 & 0 & 0 \\ - 0 & 1 & 0 & 0 \\ - 0 & 0 & 1 & 0 \\ - 0 & 0 & 0 & 1 - \end{bmatrix} -$$ +There are several central software libraries for linear algebra and eigenvalue problems. Several of the more +popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used +software package LAPACK, which follows two other popular packages +developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here. -

    -The inverse of a matrix is defined by +

      +
    • LINPACK: package for linear equations and least square problems.
      • +
    • LAPACK:package for solving symmetric, unsymmetric and generalized eigenvalue problems. From LAPACK's website http://www.netlib.org it is possible to download for free all source codes from this library. Both C/C++ and Fortran versions are available.
      • +
    • BLAS (I, II and III): (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. Blas I is vector operations, II vector-matrix operations and III matrix-matrix operations. Highly parallelized and efficient codes, all available for download from http://www.netlib.org.
      • +
    -$$ -\mathbf{A}^{-1} \cdot \mathbf{A} = I -$$ - -

    - -

    -
    - - - - - - - - - - - -
    Relations Name matrix elements
    \( A=A^{T} \) symmetric \( a_{ij}=a_{ji} \)
    \( A=\left (A^{T}\right )^{-1} \) real orthogonal \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj}=\delta_{ij} \)
    \( A=A^{ * } \) real matrix \( a_{ij}=a_{ij}^{ < em>} \)
    \( A=A^{\dagger} \) hermitian \( a_{ij}=a_{ji}^{ < /em>} \)
    \( A=\left(A^{\dagger}\right )^{-1} \) unitary \( \sum_k a_{ik}a_{jk}^{ < em>}=\sum_k a_{ki}^{ < /em> } a_{kj}=\delta_{ij} \)
    -
    -
    -

    -

    -
    - - -

    diff --git a/doc/pub/week34/html/._week34-bs030.html b/doc/pub/week34/html/._week34-bs030.html index 73a9fe1e0..f6c7b3849 100644 --- a/doc/pub/week34/html/._week34-bs030.html +++ b/doc/pub/week34/html/._week34-bs030.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,20 +258,58 @@ MathJax.Hub.Config({ -

    Some famous Matrices

    +

    Basic Matrix Features

    - +

    +

    +
    +

    +$$ + \mathbf{A} = + \begin{bmatrix} a_{11} & a_{12} & a_{13} & a_{14} \\ + a_{21} & a_{22} & a_{23} & a_{24} \\ + a_{31} & a_{32} & a_{33} & a_{34} \\ + a_{41} & a_{42} & a_{43} & a_{44} + \end{bmatrix}\qquad +\mathbf{I} = + \begin{bmatrix} 1 & 0 & 0 & 0 \\ + 0 & 1 & 0 & 0 \\ + 0 & 0 & 1 & 0 \\ + 0 & 0 & 0 & 1 + \end{bmatrix} +$$ +

    +The inverse of a matrix is defined by + +$$ +\mathbf{A}^{-1} \cdot \mathbf{A} = I +$$ + +

    + +

    +
    + + + + + + + + + + + +
    Relations Name matrix elements
    \( A=A^{T} \) symmetric \( a_{ij}=a_{ji} \)
    \( A=\left (A^{T}\right )^{-1} \) real orthogonal \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj}=\delta_{ij} \)
    \( A=A^{ * } \) real matrix \( a_{ij}=a_{ij}^{ < em>} \)
    \( A=A^{\dagger} \) hermitian \( a_{ij}=a_{ji}^{ < /em>} \)
    \( A=\left(A^{\dagger}\right )^{-1} \) unitary \( \sum_k a_{ik}a_{jk}^{ < em>}=\sum_k a_{ki}^{ < /em> } a_{kj}=\delta_{ij} \)
    +
    +
    +

    +

    +
    + + +

    diff --git a/doc/pub/week34/html/._week34-bs031.html b/doc/pub/week34/html/._week34-bs031.html index 6e39114c2..a816b9c0e 100644 --- a/doc/pub/week34/html/._week34-bs031.html +++ b/doc/pub/week34/html/._week34-bs031.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,27 +258,20 @@ MathJax.Hub.Config({ -

    More Basic Matrix Features

    - -

    -

    -
    -

    -For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all equivalent +

    Some famous Matrices

      -
    • If the inverse of \( \mathbf{A} \) exists, \( \mathbf{A} \) is nonsingular.
      • -
    • The equation \( \mathbf{Ax}=0 \) implies \( \mathbf{x}=0 \).
      • -
    • The rows of \( \mathbf{A} \) form a basis of \( R^N \).
      • -
    • The columns of \( \mathbf{A} \) form a basis of \( R^N \).
      • -
    • \( \mathbf{A} \) is a product of elementary matrices.
      • -
    • \( 0 \) is not eigenvalue of \( \mathbf{A} \).
      • +
    • Diagonal if \( a_{ij}=0 \) for \( i\ne j \)
      • +
    • Upper triangular if \( a_{ij}=0 \) for \( i>j \)
      • +
    • Lower triangular if \( a_{ij}=0 \) for \( i < j \)
      • +
    • Upper Hessenberg if \( a_{ij}=0 \) for \( i>j+1 \)
      • +
    • Lower Hessenberg if \( a_{ij}=0 \) for \( i < j+1 \)
      • +
    • Tridiagonal if \( a_{ij}=0 \) for \( |i -j|>1 \)
      • +
    • Lower banded with bandwidth \( p \): \( a_{ij}=0 \) for \( i>j+p \)
      • +
    • Upper banded with bandwidth \( p \): \( a_{ij}=0 \) for \( i < j+p \)
      • +
    • Banded, block upper triangular, block lower triangular....
    -
    -
    - -

    diff --git a/doc/pub/week34/html/._week34-bs032.html b/doc/pub/week34/html/._week34-bs032.html index 56a276103..863fd17f5 100644 --- a/doc/pub/week34/html/._week34-bs032.html +++ b/doc/pub/week34/html/._week34-bs032.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,90 +258,26 @@ MathJax.Hub.Config({ -

    Numpy and arrays

    -Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as +

    More Basic Matrix Features

    +

    +
    +

    +For an \( N\times N \) matrix \( \mathbf{A} \) the following properties are all equivalent - -

    import numpy as np
-
    -

    -Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution, -

    +

      +
    • If the inverse of \( \mathbf{A} \) exists, \( \mathbf{A} \) is nonsingular.
      • +
    • The equation \( \mathbf{Ax}=0 \) implies \( \mathbf{x}=0 \).
      • +
    • The rows of \( \mathbf{A} \) form a basis of \( R^N \).
      • +
    • The columns of \( \mathbf{A} \) form a basis of \( R^N \).
      • +
    • \( \mathbf{A} \) is a product of elementary matrices.
      • +
    • \( 0 \) is not eigenvalue of \( \mathbf{A} \).
      • +
    +
    +
    - -
    n = 10
-x = np.random.normal(size=n)
-print(x)
-
    -

    -We defined a vector \( x \) with \( n=10 \) elements with its values given by the Normal distribution \( N(0,1) \). -Another alternative is to declare a vector as follows -

    - -

    import numpy as np
-x = np.array([1, 2, 3])
-print(x)
-
    -

    -Here we have defined a vector with three elements, with \( x_0=1 \), \( x_1=2 \) and \( x_2=3 \). Note that both Python and C++ -start numbering array elements from \( 0 \) and on. This means that a vector with \( n \) elements has a sequence of entities \( x_0, x_1, x_2, \dots, x_{n-1} \). We could also let (recommended) Numpy to compute the logarithms of a specific array as -

    - - -

    import numpy as np
-x = np.log(np.array([4, 7, 8]))
-print(x)
-
    -

    -In the last example we used Numpy's unary function \( np.log \). This function is -highly tuned to compute array elements since the code is vectorized -and does not require looping. We normaly recommend that you use the -Numpy intrinsic functions instead of the corresponding log function -from Python's math module. The looping is done explicitely by the -np.log function. The alternative, and slower way to compute the -logarithms of a vector would be to write - -

    - - -

    import numpy as np
-from math import log
-x = np.array([4, 7, 8])
-for i in range(0, len(x)):
-    x[i] = log(x[i])
-print(x)
-
    -

    -We note that our code is much longer already and we need to import the log function from the math module. -The attentive reader will also notice that the output is \( [1, 1, 2] \). Python interprets automagically our numbers as integers (like the automatic keyword in C++). To change this we could define our array elements to be double precision numbers as -

    - - -

    import numpy as np
-x = np.log(np.array([4, 7, 8], dtype = np.float64))
-print(x)
-
    -

    -or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is -

    - - -

    import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0]))
-print(x)
-
    -

    -To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the itemsize functionality (the array \( x \) is actually an object which inherits the functionalities defined in Numpy) as -

    - - -

    import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0]))
-print(x.itemsize)
-

    @@ -363,6 +301,7 @@ x = np.l

  • 38
  • 39
  • 40
    • +
  • 41
  • »
    • diff --git a/doc/pub/week34/html/._week34-bs033.html b/doc/pub/week34/html/._week34-bs033.html index 944b9a628..7383d8035 100644 --- a/doc/pub/week34/html/._week34-bs033.html +++ b/doc/pub/week34/html/._week34-bs033.html @@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source ('Reading Recommendations', 2, None, 'reading-recommendations'), ('Thursday August 26', 2, None, 'thursday-august-26'), ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'), + ('Announcement', 2, None, 'announcement'), ('Course Format', 2, None, 'course-format'), ('Teachers', 2, None, 'teachers'), ('Deadlines for projects (tentative)', @@ -198,49 +199,50 @@ MathJax.Hub.Config({
  • Reading Recommendations
  • Thursday August 26
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • Recommended textbooks
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Essential elements of ML
    • -
  • An optimization/minimization problem
    • -
  • A Frequentist approach to data analysis
    • -
  • What is a good model?
    • -
  • What is a good model? Can we define it?
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 27
    • -
  • Reading Data and fitting
    • -
  • Friday August 27
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • -
  • Exercises for week 36
    • -
  • Exercise 1: Setting up various Python environments
    • -
  • Exercise 2: making your own data and exploring scikit-learn
    • -
  • Exercise 3: Normalizing our data
    • +
  • Announcement
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Essential elements of ML
    • +
  • An optimization/minimization problem
    • +
  • A Frequentist approach to data analysis
    • +
  • What is a good model?
    • +
  • What is a good model? Can we define it?
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 27
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • +
  • Exercises for week 36
    • +
  • Exercise 1: Setting up various Python environments
    • +
  • Exercise 2: making your own data and exploring scikit-learn
    • +
  • Exercise 3: Normalizing our data
    • @@ -256,141 +258,89 @@ MathJax.Hub.Config({ -

    Matrices in Python

    - -

    -Having defined vectors, we are now ready to try out matrices. We can -define a \( 3 \times 3 \) real matrix \( \hat{A} \) as (recall that we user -lowercase letters for vectors and uppercase letters for matrices) +

    Numpy and arrays

    +Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as 

    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-print(A)

    -If we use the shape function we would get \( (3, 3) \) as output, that is verifying that our matrix is a \( 3\times 3 \) matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as +Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution,

    -

    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-# print the first column, row-major order and elements start with 0
-print(A[:,0]) 
-
    -

    -We can continue this was by printing out other columns or rows. The example here prints out the second column -

    - - -

    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-# print the first column, row-major order and elements start with 0
-print(A[1,:]) 
-
    -

    -Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the Numpy website for more details. Useful functions when defining a matrix are the np.zeros function which declares a matrix of a given dimension and sets all elements to zero -

    - - -

    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to zero
-A = np.zeros( (n, n) )
-print(A) 
-
    -

    -or initializing all elements to -

    - - -

    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to one
-A = np.ones( (n, n) )
-print(A) 
-
    -

    -or as unitarily distributed random numbers (see the material on random number generators in the statistics part) -

    - - -

    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]
-A = np.random.rand(n, n)
-print(A) 
-
    -

    -As we will see throughout these lectures, there are several extremely useful functionalities in Numpy. -As an example, consider the discussion of the covariance matrix. Suppose we have defined three vectors -\( \hat{x}, \hat{y}, \hat{z} \) with \( n \) elements each. The covariance matrix is defined as -$$ -\hat{\Sigma} = \begin{bmatrix} \sigma_{xx} & \sigma_{xy} & \sigma_{xz} \\ - \sigma_{yx} & \sigma_{yy} & \sigma_{yz} \\ - \sigma_{zx} & \sigma_{zy} & \sigma_{zz} - \end{bmatrix}, -$$ - -where for example -$$ -\sigma_{xy} =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -$$ - -The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. -The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \hat{W} \) -$$ -\hat{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} - \end{bmatrix}, -$$ - -

    -which in turn is converted into into the \( 3\times 3 \) covariance matrix -\( \hat{\Sigma} \) via the Numpy function np.cov(). We note that we can also calculate -the mean value of each set of samples \( \hat{x} \) etc using the Numpy -function np.mean(x). We can also extract the eigenvalues of the -covariance matrix through the np.linalg.eig() function. - -

    - - -

    # Importing various packages
-import numpy as np
-
-n = 100
+
    n = 10
 x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-z = x**3+np.random.normal(size=n)
-print(np.mean(z))
-W = np.vstack((x, y, z))
-Sigma = np.cov(W)
-print(Sigma)
-Eigvals, Eigvecs = np.linalg.eig(Sigma)
-print(Eigvals)
+print(x)
 
    
 
    
+We defined a vector \( x \) with \( n=10 \) elements with its values given by the Normal distribution \( N(0,1) \).
+Another alternative is to declare a vector as follows
+
    
 
 
 
    import numpy as np
-import matplotlib.pyplot as plt
-from scipy import sparse
-eye = np.eye(4)
-print(eye)
-sparse_mtx = sparse.csr_matrix(eye)
-print(sparse_mtx)
-x = np.linspace(-10,10,100)
-y = np.sin(x)
-plt.plot(x,y,marker='x')
-plt.show()
+x = np.array([1, 2, 3])
+print(x)
+
    
+
    
+Here we have defined a vector with three elements, with \( x_0=1 \), \( x_1=2 \) and \( x_2=3 \). Note that both Python and C++
+start numbering array elements from \( 0 \) and on. This means that a vector with \( n \) elements has a sequence of entities \( x_0, x_1, x_2, \dots, x_{n-1} \). We could also let (recommended) Numpy to compute the logarithms of a specific array as
+
    
+
+
+
    import numpy as np
+x = np.log(np.array([4, 7, 8]))
+print(x)
+
    
+
    
+In the last example we used Numpy's unary function \( np.log \). This function is
+highly tuned to compute array elements since the code is vectorized
+and does not require looping. We normaly recommend that you use the
+Numpy intrinsic functions instead of the corresponding log function
+from Python's math module. The looping is done explicitely by the
+np.log function. The alternative, and slower way to compute the
+logarithms of a vector would be to write
+
+
    
+
+
+
    import numpy as np
+from math import log
+x = np.array([4, 7, 8])
+for i in range(0, len(x)):
+    x[i] = log(x[i])
+print(x)
+
    
+
    
+We note that our code is much longer already and we need to import the log function from the math module. 
+The attentive reader will also notice that the output is \( [1, 1, 2] \). Python interprets automagically our numbers as integers (like the automatic keyword in C++). To change this we could define our array elements to be double precision numbers as
+
    
+
+
+
    import numpy as np
+x = np.log(np.array([4, 7, 8], dtype = np.float64))
+print(x)
+
    
+
    
+or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is
+
    
+
+
+
    import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x)
+
    
+
    
+To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the itemsize functionality (the array \( x \) is actually an object which inherits the functionalities defined in Numpy) as 
+
    
+
+
+
    import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x.itemsize)
 
    
 
    
 
    
@@ -414,6 +364,7 @@ plt.show()
   
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+  
  • 41
    • 
   
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    • 
 
 
diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html
index 6e80662a5..131104471 100644
--- a/doc/pub/week34/html/week34-bs.html
+++ b/doc/pub/week34/html/week34-bs.html
@@ -45,6 +45,7 @@ Automatically generated HTML file from DocOnce source
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
               ('Thursday August 26', 2, None, 'thursday-august-26'),
               ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
+              ('Announcement', 2, None, 'announcement'),
               ('Course Format', 2, None, 'course-format'),
               ('Teachers', 2, None, 'teachers'),
               ('Deadlines for projects (tentative)',
@@ -198,49 +199,50 @@ MathJax.Hub.Config({
       
  • Reading Recommendations
    • 
       
  • Thursday August 26
    • 
       
  • Lectures and ComputerLab
    • 
-      
  • Course Format
    • 
-      
  • Teachers
    • 
-      
  • Deadlines for projects (tentative)
    • 
-      
  • Recommended textbooks
    • 
-      
  • Prerequisites
    • 
-      
  • Learning outcomes
    • 
-      
  • Topics covered in this course: Statistical analysis and optimization of data
    • 
-      
  • Topics covered in this course: Machine Learning
    • 
-      
  • Extremely useful tools, strongly recommended
    • 
-      
  • Other courses on Data science and Machine Learning  at UiO
    • 
-      
  • Introduction
    • 
-      
  • What is Machine Learning?
    • 
-      
  • Types of Machine Learning
    • 
-      
  • Essential elements of ML
    • 
-      
  • An optimization/minimization problem
    • 
-      
  • A Frequentist approach to data analysis
    • 
-      
  • What is a good model?
    • 
-      
  • What is a good model? Can we define it?
    • 
-      
  • Software and needed installations
    • 
-      
  • Python installers
    • 
-      
  • Useful Python libraries
    • 
-      
  • Installing R, C++, cython or Julia
    • 
-      
  • Installing R, C++, cython, Numba etc
    • 
-      
  • Numpy examples and Important Matrix and vector handling packages
    • 
-      
  • Basic Matrix Features
    • 
-      
  •    Some famous Matrices
    • 
-      
  •    More Basic Matrix Features
    • 
-      
  • Numpy and arrays
    • 
-      
  • Matrices in Python
    • 
-      
  • Meet the Pandas
    • 
-      
  • Friday August 27
    • 
-      
  • Reading Data and fitting
    • 
-      
  • Friday August 27
    • 
-      
  •    Simple linear regression model using scikit-learn
    • 
-      
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • 
-      
  •    Organizing our data
    • 
-      
  •    Seeing the wood for the trees
    • 
-      
  •    And what about using neural networks?
    • 
-      
  • A first summary
    • 
-      
  • Exercises for week 36
    • 
-      
  • Exercise 1: Setting up various Python environments
    • 
-      
  • Exercise 2: making your own data and exploring scikit-learn
    • 
-      
  • Exercise 3: Normalizing our data
    • 
+      
  • Announcement
    • 
+      
  • Course Format
    • 
+      
  • Teachers
    • 
+      
  • Deadlines for projects (tentative)
    • 
+      
  • Recommended textbooks
    • 
+      
  • Prerequisites
    • 
+      
  • Learning outcomes
    • 
+      
  • Topics covered in this course: Statistical analysis and optimization of data
    • 
+      
  • Topics covered in this course: Machine Learning
    • 
+      
  • Extremely useful tools, strongly recommended
    • 
+      
  • Other courses on Data science and Machine Learning  at UiO
    • 
+      
  • Introduction
    • 
+      
  • What is Machine Learning?
    • 
+      
  • Types of Machine Learning
    • 
+      
  • Essential elements of ML
    • 
+      
  • An optimization/minimization problem
    • 
+      
  • A Frequentist approach to data analysis
    • 
+      
  • What is a good model?
    • 
+      
  • What is a good model? Can we define it?
    • 
+      
  • Software and needed installations
    • 
+      
  • Python installers
    • 
+      
  • Useful Python libraries
    • 
+      
  • Installing R, C++, cython or Julia
    • 
+      
  • Installing R, C++, cython, Numba etc
    • 
+      
  • Numpy examples and Important Matrix and vector handling packages
    • 
+      
  • Basic Matrix Features
    • 
+      
  •    Some famous Matrices
    • 
+      
  •    More Basic Matrix Features
    • 
+      
  • Numpy and arrays
    • 
+      
  • Matrices in Python
    • 
+      
  • Meet the Pandas
    • 
+      
  • Friday August 27
    • 
+      
  • Reading Data and fitting
    • 
+      
  • Friday August 27
    • 
+      
  •    Simple linear regression model using scikit-learn
    • 
+      
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • 
+      
  •    Organizing our data
    • 
+      
  •    Seeing the wood for the trees
    • 
+      
  •    And what about using neural networks?
    • 
+      
  • A first summary
    • 
+      
  • Exercises for week 36
    • 
+      
  • Exercise 1: Setting up various Python environments
    • 
+      
  • Exercise 2: making your own data and exploring scikit-learn
    • 
+      
  • Exercise 3: Normalizing our data
    • 
 
         
       
@@ -299,7 +301,7 @@ MathJax.Hub.Config({
   
  • 9
    • 
   
  • 10
    • 
   
  • ...
    • 
-  
  • 40
    • 
+  
  • 41
    • 
   
  • »
    • 
 
 
diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html
index 65ed570ca..6e7b71fb4 100644
--- a/doc/pub/week34/html/week34-reveal.html
+++ b/doc/pub/week34/html/week34-reveal.html
@@ -237,6 +237,14 @@ The lectures will be recorded and updated videos will be posted after the lectur
 
 
 
+
    
+
    Announcement 
    
+
+
    
+NORA AI competetion:  See the link here https://www.nora.ai/Competition/image-segmentation.html
+
    
+
+
 
    
 
    Course Format 
    
 
@@ -313,9 +321,9 @@ The lectures will be recorded and updated videos will be posted after the lectur
 
    
 
 
      
-
    1.  Project 1: September 27 (graded with feedback)
      2. 
-
    2.  Project 2: November 1 (graded with feedback)
      3. 
-
    3.  Project 3: December 6  (graded with feedback)
      4. 
+
    4.  Project 1: October 4 (available September 10) graded with feedback)
      5. 
+
    5.  Project 2: November 8 (available October 8, graded with feedback)
      6. 
+
    6.  Project 3: December 13  (available November 12, graded with feedback)
      7. 
 
    
 
    
 
diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html
index 22adb2008..9b2a0dac5 100644
--- a/doc/pub/week34/html/week34-solarized.html
+++ b/doc/pub/week34/html/week34-solarized.html
@@ -65,6 +65,7 @@ div { text-align: justify; text-justify: inter-word; }
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
               ('Thursday August 26', 2, None, 'thursday-august-26'),
               ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
+              ('Announcement', 2, None, 'announcement'),
               ('Course Format', 2, None, 'course-format'),
               ('Teachers', 2, None, 'teachers'),
               ('Deadlines for projects (tentative)',
@@ -294,6 +295,14 @@ The lectures will be recorded and updated videos will be posted after the lectur
 
    
 
 
+
    
+









    
+
+
    Announcement 
    
+
+
    
+NORA AI competetion:  See the link here https://www.nora.ai/Competition/image-segmentation.html
+
 
    
 









    
 
@@ -373,9 +382,9 @@ The lectures will be recorded and updated videos will be posted after the lectur
 
    
 
 
      
-
    1.  Project 1: September 27 (graded with feedback)
      2. 
-
    2.  Project 2: November 1 (graded with feedback)
      3. 
-
    3.  Project 3: December 6  (graded with feedback)
      4. 
+
    4.  Project 1: October 4 (available September 10) graded with feedback)
      5. 
+
    5.  Project 2: November 8 (available October 8, graded with feedback)
      6. 
+
    6.  Project 3: December 13  (available November 12, graded with feedback)
      7. 
 
    
 
 Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas.
diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html
index 90572a6a0..be7f92229 100644
--- a/doc/pub/week34/html/week34.html
+++ b/doc/pub/week34/html/week34.html
@@ -70,6 +70,7 @@ div { text-align: justify; text-justify: inter-word; }
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
               ('Thursday August 26', 2, None, 'thursday-august-26'),
               ('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
+              ('Announcement', 2, None, 'announcement'),
               ('Course Format', 2, None, 'course-format'),
               ('Teachers', 2, None, 'teachers'),
               ('Deadlines for projects (tentative)',
@@ -299,6 +300,14 @@ The lectures will be recorded and updated videos will be posted after the lectur
    +

    +









    + +

    Announcement

    + +

    +NORA AI competetion: See the link here https://www.nora.ai/Competition/image-segmentation.html +











    @@ -378,9 +387,9 @@ The lectures will be recorded and updated videos will be posted after the lectur

      -
    1. Project 1: September 27 (graded with feedback)
      2. -
    2. Project 2: November 1 (graded with feedback)
      3. -
    3. Project 3: December 6 (graded with feedback)
      4. +
    4. Project 1: October 4 (available September 10) graded with feedback)
      5. +
    5. Project 2: November 8 (available October 8, graded with feedback)
      6. +
    6. Project 3: December 13 (available November 12, graded with feedback)
    Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas. diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz index 24fee0d8df744ddd2c094b8bff02b27e8ab0bbfd..714b8d9557b4dde5d43e2c2f24c21193383b1ee6 100644 GIT binary patch delta 20 ccmdnGoNeoJHa7Wg4u+SWjci-l7}rk*08Uf~Gynhq delta 20 ccmdnGoNeoJHa7Wg4u-|;M1& diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index 3635c06b1..9aa50e8f0 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -75,6 +75,10 @@ "\n", "\n", "\n", + "## Announcement\n", + "\n", + "**NORA AI competetion:** See the link here \n", + "\n", "\n", "\n", "## Course Format\n", @@ -135,11 +139,11 @@ "## Deadlines for projects (tentative)\n", "\n", "\n", - "1. Project 1: September 27 (graded with feedback)\n", + "1. Project 1: October 4 (available September 10) graded with feedback)\n", "\n", - "2. Project 2: November 1 (graded with feedback)\n", + "2. Project 2: November 8 (available October 8, graded with feedback)\n", "\n", - "3. Project 3: December 6 (graded with feedback)\n", + "3. Project 3: December 13 (available November 12, graded with feedback)\n", "\n", "Projects are handed in using **Canvas**. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via **Canvas**.\n", "\n", diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt index ddd117802..7bafbd0be 100644 --- a/doc/src/week34/week34.do.txt +++ b/doc/src/week34/week34.do.txt @@ -53,6 +53,11 @@ The lectures will be recorded and updated videos will be posted after the lectur !eblock +!split +===== Announcement ===== + +_NORA AI competetion:_ See the link here URL:"https://www.nora.ai/Competition/image-segmentation.html" + !split @@ -98,9 +103,9 @@ _Teachers :_ !bblock -o Project 1: September 27 (graded with feedback) -o Project 2: November 1 (graded with feedback) -o Project 3: December 6 (graded with feedback) +o Project 1: October 4 (available September 10) graded with feedback) +o Project 2: November 8 (available October 8, graded with feedback) +o Project 3: December 13 (available November 12, graded with feedback) Projects are handed in using _Canvas_. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via _Canvas_.