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updating course schedule

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mhjensen
2020-09-16 10:51:03 +02:00
parent 9522add63d
commit 8b7b993103
4 changed files with 271 additions and 651 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/web/course.dlog
+3
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@@ -135,3 +135,6 @@ output in course.html
running mako on course.do.txt to make tmp_mako__course.do.txt
translating doconce text in tmp_mako__course.do.txt to html
output in course.html
running mako on course.do.txt to make tmp_mako__course.do.txt
translating doconce text in tmp_mako__course.do.txt to html
output in course.html
doc/web/course.do.txt
+25 -25
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@@ -1,30 +1,26 @@
TITLE: Overview of course material: Data Analysis and Machine Learning
TITLE: Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics (office FV308), University of Oslo, Norway
<%
pub_url = 'https://compphysics.github.io/MachineLearning/doc/pub'
published = ['Intro2Course', 'Introduction', 'How2ReadData', 'Linalg', 'Statistics', 'Regression', 'Splines', 'LogReg', 'NeuralNet', 'cnn', 'DimRed', 'DecisionTrees', 'svm', 'BM', 'Recurrent', 'Autoencoders', 'Reinforce', 'odenn', 'Bayesian', 'summary',]
published = ['week34', 'week35', 'week36', 'week37', 'week38', 'week39', 'week40', 'week41', 'week42', 'week43', 'week44', 'week45', 'week46', 'week47', 'week48', ]
chapters = {
'Intro2Course': 'Basic introduction to the course with schedule etc',
'Introduction': 'Introduction to Data Analysis and Machine Learning',
'How2ReadData': 'Getting started with Machine Learning with simple Examples',
'Linalg': 'Review of central linear algebra elements',
'Statistics': 'Monte Carlo methods and elements of probability theory',
'Regression': 'Regression Methods',
'Splines': 'Gradient methods and Minimization Algorithms',
'LogReg': 'Logistic Regression',
'NeuralNet': 'Neural Networks',
'cnn': 'Convolutional Neural Networks',
'DimRed': 'Reduction of dimensionality',
'DecisionTrees': 'Decision Trees and Random Forests',
'svm': 'Support Vector Machines',
'BM': 'Unsupervised Learning, Boltzmann Machines',
'Recurrent': 'Recurrent Neural Networks',
'Autoencoders': 'Autoencoders',
'Reinforce': 'Reinforcement Learning',
'odenn': 'Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks',
'Bayesian': 'Elements of Bayesian theory and Bayesian Neural Networks',
'summary': 'Summary',
'week34': 'Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression',
'week35': 'Week35 August 24-28: Linear regression and review of statistics and probability theory',
'week36': 'Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression',
'week37': 'Week37 September 7-11: Ridge and Lasso Regression',
'week38': 'Week38 September 14-18: Summary of linear regression methods and start Logistic Regression',
'week39': 'Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks',
'week40': 'Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model',
'week41': 'Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)',
'week42': 'Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks',
'week43': 'Week43 October 19-23: Dimesionality Reduction, Principal Component analysis',
'week44': 'Week44 October 26-30: Decision Trees and Bagging',
'week45': 'Week45 November 2-6: Random Forests and Gradient Boosting',
'week46': 'Week46 November 9-13: Support Vector Machines',
'week47': 'Week47 November 16-20: Support Vector Machines',
'week48': 'Week48 November 23-27: Unsupervised learning, clustering and summary of course',
}
%>
@@ -34,9 +30,6 @@ chapters = {
<%def name="text_types(name)">
* LaTeX PDF:
* For printing:
* "Standard one-page format": "${pub_url}/${name}/pdf/${name}-minted.pdf"
* HTML:
* "Plain html": "${pub_url}/${name}/html/${name}.html"
* "reveal.js beige slide style": "${pub_url}/${name}/html/${name}-reveal.html"
@@ -96,6 +89,13 @@ ${text_types(ch)}
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/html/hw2-bs.html"
=== Textbook ===
* PDF-file:
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/LectureNotes/pdf/book.pdf"
* Jupyter notebook:
* "ipynb file": "http://compphysics.github.io/MachineLearning/doc/LectureNotes/ipynb/book.ipynb"
=== Project 1, Deadline October 5 (available September 1) ===
* LaTeX and PDF:
doc/web/course.html
+160 -430
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@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="Overview of course material: Data Analysis and Machine Learning">
<meta name="description" content="Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)">
<title>Overview of course material: Data Analysis and Machine Learning</title>
<title>Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)</title>
<style type="text/css">
@@ -69,66 +69,94 @@ div { text-align: justify; text-justify: inter-word; }
<!-- tocinfo
{'highest level': 2,
'sections': [('Basic introduction to the course with schedule etc',
'sections': [('Week34 August 17-21:Basic introduction to the course with '
'schedule etc and start Linear Regression',
2,
None,
'___sec0'),
('Introduction to Data Analysis and Machine Learning',
('Week35 August 24-28: Linear regression and review of '
'statistics and probability theory',
2,
None,
'___sec1'),
('Getting started with Machine Learning with simple Examples',
('Week36 August 31- September 4: Resampling techniques, '
'Cross-validation and Bootstrap and start discussion of Ridge '
'regression',
2,
None,
'___sec2'),
('Review of central linear algebra elements', 2, None, '___sec3'),
('Monte Carlo methods and elements of probability theory',
('Week37 September 7-11: Ridge and Lasso Regression',
2,
None,
'___sec3'),
('Week38 September 14-18: Summary of linear regression methods '
'and start Logistic Regression',
2,
None,
'___sec4'),
('Regression Methods', 2, None, '___sec5'),
('Gradient methods and Minimization Algorithms',
('Week39 September 21-25: Logistic Regression and Gradient '
'methods. Start Neural Networks',
2,
None,
'___sec5'),
('Week40 September 28 - October 2: Neural Networks, building a '
'multi-layer Perceptron model',
2,
None,
'___sec6'),
('Logistic Regression', 2, None, '___sec7'),
('Neural Networks', 2, None, '___sec8'),
('Convolutional Neural Networks', 2, None, '___sec9'),
('Reduction of dimensionality', 2, None, '___sec10'),
('Decision Trees and Random Forests', 2, None, '___sec11'),
('Support Vector Machines', 2, None, '___sec12'),
('Unsupervised Learning, Boltzmann Machines',
('Week41 October 5-9: Introduction to Tensorflow and deep '
'learning (Convolutional Neural Networks and Recurrent Neural '
'Networks)',
2,
None,
'___sec7'),
('Week42 October 12-16: Deep learning (Convolutional Neural '
'Networks and Recurrent Neural Networks',
2,
None,
'___sec8'),
('Week43 October 19-23: Dimesionality Reduction, Principal '
'Component analysis',
2,
None,
'___sec9'),
('Week44 October 26-30: Decision Trees and Bagging',
2,
None,
'___sec10'),
('Week45 November 2-6: Random Forests and Gradient Boosting',
2,
None,
'___sec11'),
('Week46 November 9-13: Support Vector Machines',
2,
None,
'___sec12'),
('Week47 November 16-20: Support Vector Machines',
2,
None,
'___sec13'),
('Recurrent Neural Networks', 2, None, '___sec14'),
('Autoencoders', 2, None, '___sec15'),
('Reinforcement Learning', 2, None, '___sec16'),
('Solving ordinary and Partial Differential Equations and '
'Eigenvalue Problems with Neural Networks',
('Week48 November 23-27: Unsupervised learning, clustering and '
'summary of course',
2,
None,
'___sec17'),
('Elements of Bayesian theory and Bayesian Neural Networks',
2,
None,
'___sec18'),
('Summary', 2, None, '___sec19'),
('Projects and Exercises Fall 2020', 2, None, '___sec20'),
('First homework set, week 35', 3, None, '___sec21'),
('Second homework set, week 36', 3, None, '___sec22'),
'___sec14'),
('Projects and Exercises Fall 2020', 2, None, '___sec15'),
('First homework set, week 35', 3, None, '___sec16'),
('Second homework set, week 36', 3, None, '___sec17'),
('Textbook', 3, None, '___sec18'),
('Project 1, Deadline October 5 (available September 1)',
3,
None,
'___sec23'),
'___sec19'),
('Project 2, Deadline November 2 (available September 28)',
3,
None,
'___sec24'),
'___sec20'),
('Project 3, Deadline December 7 (available November 2)',
3,
None,
'___sec25')]}
'___sec21')]}
end of tocinfo -->
<body>
@@ -138,7 +166,7 @@ end of tocinfo -->
<center><h1>Overview of course material: Data Analysis and Machine Learning</h1></center> <!-- document title -->
<center><h1>Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)</h1></center> <!-- document title -->
<p>
<!-- author(s): <a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a> -->
@@ -174,611 +202,296 @@ formulas in HTML or ipython notebook files.
</div>
<h2 id="___sec0">Basic introduction to the course with schedule etc </h2>
<h2 id="___sec0">Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week34/ipynb/week34.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec1">Introduction to Data Analysis and Machine Learning </h2>
<h2 id="___sec1">Week35 August 24-28: Linear regression and review of statistics and probability theory </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Introduction/pdf/Introduction-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Introduction/ipynb/Introduction.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week35/ipynb/week35.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec2">Getting started with Machine Learning with simple Examples </h2>
<h2 id="___sec2">Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week36/ipynb/week36.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec3">Review of central linear algebra elements </h2>
<h2 id="___sec3">Week37 September 7-11: Ridge and Lasso Regression </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Linalg/pdf/Linalg-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Linalg/ipynb/Linalg.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week37/ipynb/week37.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec4">Monte Carlo methods and elements of probability theory </h2>
<h2 id="___sec4">Week38 September 14-18: Summary of linear regression methods and start Logistic Regression </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/pdf/Statistics-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/ipynb/Statistics.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week38/ipynb/week38.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec5">Regression Methods </h2>
<h2 id="___sec5">Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/pdf/Regression-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/ipynb/Regression.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week39/ipynb/week39.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec6">Gradient methods and Minimization Algorithms </h2>
<h2 id="___sec6">Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/pdf/Splines-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/ipynb/Splines.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week40/ipynb/week40.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec7">Logistic Regression </h2>
<h2 id="___sec7">Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/LogReg/pdf/LogReg-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/LogReg/ipynb/LogReg.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week41/ipynb/week41.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec8">Neural Networks </h2>
<h2 id="___sec8">Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/pdf/NeuralNet-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/ipynb/week42.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec9">Convolutional Neural Networks </h2>
<h2 id="___sec9">Week43 October 19-23: Dimesionality Reduction, Principal Component analysis </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/cnn/pdf/cnn-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/cnn/ipynb/cnn.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week43/ipynb/week43.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec10">Reduction of dimensionality </h2>
<h2 id="___sec10">Week44 October 26-30: Decision Trees and Bagging </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/pdf/DimRed-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DimRed/ipynb/DimRed.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week44/ipynb/week44.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec11">Decision Trees and Random Forests </h2>
<h2 id="___sec11">Week45 November 2-6: Random Forests and Gradient Boosting </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week45/ipynb/week45.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec12">Support Vector Machines </h2>
<h2 id="___sec12">Week46 November 9-13: Support Vector Machines </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/svm/pdf/svm-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/svm/ipynb/svm.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week46/ipynb/week46.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec13">Unsupervised Learning, Boltzmann Machines </h2>
<h2 id="___sec13">Week47 November 16-20: Support Vector Machines </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/pdf/BM-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/BM/ipynb/BM.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week47/ipynb/week47.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec14">Recurrent Neural Networks </h2>
<h2 id="___sec14">Week48 November 23-27: Unsupervised learning, clustering and summary of course </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/pdf/Recurrent-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/ipynb/Recurrent.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec15">Autoencoders </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/pdf/Autoencoders-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/ipynb/Autoencoders.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec16">Reinforcement Learning </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/pdf/Reinforce-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/ipynb/Reinforce.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec17">Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/pdf/odenn-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/odenn/ipynb/odenn.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec18">Elements of Bayesian theory and Bayesian Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/pdf/Bayesian-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/ipynb/Bayesian.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h2 id="___sec19">Summary </h2>
<ul>
<li> LaTeX PDF:</li>
<ul>
<li> For printing:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/summary/pdf/summary-minted.pdf" target="_self">Standard one-page format</a></li>
</ul>
</ul>
<li> HTML:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary.html" target="_self">Plain html</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary-reveal.html" target="_self">reveal.js beige slide style</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/summary/ipynb/summary.ipynb" target="_self">ipynb file</a></li>
<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/week48/ipynb/week48.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<!-- !split -->
<h2 id="___sec20">Projects and Exercises Fall 2020 </h2>
<h2 id="___sec15">Projects and Exercises Fall 2020 </h2>
<h3 id="___sec21">First homework set, week 35 </h3>
<h3 id="___sec16">First homework set, week 35 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -797,7 +510,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec22">Second homework set, week 36 </h3>
<h3 id="___sec17">Second homework set, week 36 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -816,7 +529,24 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec23">Project 1, Deadline October 5 (available September 1) </h3>
<h3 id="___sec18">Textbook </h3>
<ul>
<li> PDF-file:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/LectureNotes/pdf/book.pdf" target="_self">PDF file</a></li>
</ul>
<li> Jupyter notebook:</li>
<ul>
<li> <a href="http://compphysics.github.io/MachineLearning/doc/LectureNotes/ipynb/book.ipynb" target="_self">ipynb file</a></li>
</ul>
</ul>
<h3 id="___sec19">Project 1, Deadline October 5 (available September 1) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -841,7 +571,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec24">Project 2, Deadline November 2 (available September 28) </h3>
<h3 id="___sec20">Project 2, Deadline November 2 (available September 28) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -866,7 +596,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec25">Project 3, Deadline December 7 (available November 2) </h3>
<h3 id="___sec21">Project 3, Deadline December 7 (available November 2) </h3>
<ul>
<li> LaTeX and PDF:</li>
doc/web/tmp_mako__course.do.txt
+83 -196
View File
@@ -1,4 +1,4 @@
TITLE: Overview of course material: Data Analysis and Machine Learning
TITLE: Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics (office FV308), University of Oslo, Norway
@@ -29,304 +29,184 @@ formulas in HTML or ipython notebook files.
===== Basic introduction to the course with schedule etc =====
===== Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week34/html/week34-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week34/ipynb/week34.ipynb"
===== Introduction to Data Analysis and Machine Learning =====
===== Week35 August 24-28: Linear regression and review of statistics and probability theory =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/pdf/Introduction-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/ipynb/Introduction.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week35/ipynb/week35.ipynb"
===== Getting started with Machine Learning with simple Examples =====
===== Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week36/html/week36-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week36/ipynb/week36.ipynb"
===== Review of central linear algebra elements =====
===== Week37 September 7-11: Ridge and Lasso Regression =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/pdf/Linalg-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week37/html/week37-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/ipynb/Linalg.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week37/ipynb/week37.ipynb"
===== Monte Carlo methods and elements of probability theory =====
===== Week38 September 14-18: Summary of linear regression methods and start Logistic Regression =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/pdf/Statistics-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week38/html/week38-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/ipynb/Statistics.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week38/ipynb/week38.ipynb"
===== Regression Methods =====
===== Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/pdf/Regression-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/ipynb/Regression.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week39/ipynb/week39.ipynb"
===== Gradient methods and Minimization Algorithms =====
===== Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/pdf/Splines-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week40/html/week40-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/ipynb/Splines.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week40/ipynb/week40.ipynb"
===== Logistic Regression =====
===== Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/pdf/LogReg-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week41/html/week41-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/ipynb/LogReg.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week41/ipynb/week41.ipynb"
===== Neural Networks =====
===== Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/pdf/NeuralNet-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week42/ipynb/week42.ipynb"
===== Convolutional Neural Networks =====
===== Week43 October 19-23: Dimesionality Reduction, Principal Component analysis =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/cnn/pdf/cnn-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/cnn/html/cnn-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/cnn/ipynb/cnn.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week43/ipynb/week43.ipynb"
===== Reduction of dimensionality =====
===== Week44 October 26-30: Decision Trees and Bagging =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/pdf/DimRed-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week44/html/week44-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/ipynb/DimRed.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week44/ipynb/week44.ipynb"
===== Decision Trees and Random Forests =====
===== Week45 November 2-6: Random Forests and Gradient Boosting =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week45/html/week45-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week45/ipynb/week45.ipynb"
===== Support Vector Machines =====
===== Week46 November 9-13: Support Vector Machines =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/svm/pdf/svm-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week46/html/week46-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/svm/ipynb/svm.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week46/ipynb/week46.ipynb"
===== Unsupervised Learning, Boltzmann Machines =====
===== Week47 November 16-20: Support Vector Machines =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/BM/pdf/BM-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week47/html/week47-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/BM/ipynb/BM.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week47/ipynb/week47.ipynb"
===== Recurrent Neural Networks =====
===== Week48 November 23-27: Unsupervised learning, clustering and summary of course =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/pdf/Recurrent-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent-bs.html"
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/ipynb/Recurrent.ipynb"
===== Autoencoders =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/pdf/Autoencoders-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/ipynb/Autoencoders.ipynb"
===== Reinforcement Learning =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/pdf/Reinforce-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/ipynb/Reinforce.ipynb"
===== Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/pdf/odenn-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/ipynb/odenn.ipynb"
===== Elements of Bayesian theory and Bayesian Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/pdf/Bayesian-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/ipynb/Bayesian.ipynb"
===== Summary =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/summary/pdf/summary-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary.html"
* "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary-reveal.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/summary/ipynb/summary.ipynb"
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week48/ipynb/week48.ipynb"
@@ -353,6 +233,13 @@ formulas in HTML or ipython notebook files.
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/html/hw2-bs.html"
=== Textbook ===
* PDF-file:
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/LectureNotes/pdf/book.pdf"
* Jupyter notebook:
* "ipynb file": "http://compphysics.github.io/MachineLearning/doc/LectureNotes/ipynb/book.ipynb"
=== Project 1, Deadline October 5 (available September 1) ===
* LaTeX and PDF: