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mhjensen
2019-10-03 13:33:48 +02:00
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commit a20960aa4a
212 changed files with 27388 additions and 13783 deletions
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@@ -93,62 +93,63 @@ div { text-align: justify; text-justify: inter-word; }
'___sec6'),
('Logistic Regression', 2, None, '___sec7'),
('Neural Networks', 2, None, '___sec8'),
('Reduction of dimensionality', 2, None, '___sec9'),
('Decision Trees and Random Forests', 2, None, '___sec10'),
('Support Vector Machines', 2, None, '___sec11'),
('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',
2,
None,
'___sec12'),
('Recurrent Neural Networks', 2, None, '___sec13'),
('Autoencoders', 2, None, '___sec14'),
('Reinforcement Learning', 2, None, '___sec15'),
'___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',
2,
None,
'___sec16'),
'___sec17'),
('Elements of Bayesian theory and Bayesian Neural Networks',
2,
None,
'___sec17'),
('Summary', 2, None, '___sec18'),
('Projects and Exercises Fall 2019', 2, None, '___sec19'),
'___sec18'),
('Summary', 2, None, '___sec19'),
('Projects and Exercises Fall 2019', 2, None, '___sec20'),
('First homework set, week 35 (Available August 20)',
3,
None,
'___sec20'),
'___sec21'),
('Second homework set, week 36 (Available August 26)',
3,
None,
'___sec21'),
'___sec22'),
('Project 1, Deadline October 7 (available August 26)',
3,
None,
'___sec22'),
'___sec23'),
('Project 2, Deadline November 8 (available October 2)',
3,
None,
'___sec23'),
'___sec24'),
('Project 3, Deadline December 13 (available November 4)',
3,
None,
'___sec24'),
('Course content', 3, None, '___sec25'),
('Learning outcomes', 2, None, '___sec26'),
('Prerequisites', 2, None, '___sec27'),
('The course has two central parts', 2, None, '___sec28'),
'___sec25'),
('Course content', 3, None, '___sec26'),
('Learning outcomes', 2, None, '___sec27'),
('Prerequisites', 2, None, '___sec28'),
('The course has two central parts', 2, None, '___sec29'),
('Statistical analysis and optimization of data',
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None,
'___sec29'),
('Machine learning', 3, None, '___sec30'),
('Recommended textbooks', 2, None, '___sec31'),
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('Machine learning', 3, None, '___sec31'),
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('"Other '
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'___sec33')]}
end of tocinfo -->
<body>
@@ -472,7 +473,36 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec9">Reduction of dimensionality </h2>
<h2 id="___sec9">Convolutional Neural Networks </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-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>
</ul>
</ul>
<h2 id="___sec10">Reduction of dimensionality </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -501,7 +531,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec10">Decision Trees and Random Forests </h2>
<h2 id="___sec11">Decision Trees and Random Forests </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -530,7 +560,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec11">Support Vector Machines </h2>
<h2 id="___sec12">Support Vector Machines </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -559,7 +589,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec12">Unsupervised Learning, Boltzmann Machines </h2>
<h2 id="___sec13">Unsupervised Learning, Boltzmann Machines </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -588,7 +618,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec13">Recurrent Neural Networks </h2>
<h2 id="___sec14">Recurrent Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -617,7 +647,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec14">Autoencoders </h2>
<h2 id="___sec15">Autoencoders </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -646,7 +676,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec15">Reinforcement Learning </h2>
<h2 id="___sec16">Reinforcement Learning </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -675,7 +705,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec16">Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks </h2>
<h2 id="___sec17">Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -704,7 +734,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec17">Elements of Bayesian theory and Bayesian Neural Networks </h2>
<h2 id="___sec18">Elements of Bayesian theory and Bayesian Neural Networks </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -733,7 +763,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec18">Summary </h2>
<h2 id="___sec19">Summary </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -764,9 +794,9 @@ formulas in HTML or ipython notebook files.
<!-- !split -->
<h2 id="___sec19">Projects and Exercises Fall 2019 </h2>
<h2 id="___sec20">Projects and Exercises Fall 2019 </h2>
<h3 id="___sec20">First homework set, week 35 (Available August 20) </h3>
<h3 id="___sec21">First homework set, week 35 (Available August 20) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -785,7 +815,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec21">Second homework set, week 36 (Available August 26) </h3>
<h3 id="___sec22">Second homework set, week 36 (Available August 26) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -804,7 +834,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec22">Project 1, Deadline October 7 (available August 26) </h3>
<h3 id="___sec23">Project 1, Deadline October 7 (available August 26) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -823,7 +853,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec23">Project 2, Deadline November 8 (available October 2) </h3>
<h3 id="___sec24">Project 2, Deadline November 8 (available October 2) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -842,7 +872,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec24">Project 3, Deadline December 13 (available November 4) </h3>
<h3 id="___sec25">Project 3, Deadline December 13 (available November 4) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -861,7 +891,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec25">Course content </h3>
<h3 id="___sec26">Course content </h3>
<p>
Probability theory and statistical methods play a central role in science. Nowadays we are
@@ -880,7 +910,7 @@ tools of probability theory, the aim of this course is to expose you to central
This course covers thus topics like Monte Carlo methods and Markov chains, Bayesian statistics, error estimates, various linear methods, optimization of data and error analysis and central algorithms in machine learning.
The course has several numerical projects and numerical exercises that are meant to illustrate the theory.
<h2 id="___sec26">Learning outcomes </h2>
<h2 id="___sec27">Learning outcomes </h2>
<p>
The course introduces a variety of central algorithms and methods
@@ -897,19 +927,19 @@ essential for studies of data analysis and machine learning. The course is proje
<li> Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.</li>
</ul>
<h2 id="___sec27">Prerequisites </h2>
<h2 id="___sec28">Prerequisites </h2>
<p>
Basic knowledge in programming and numerics. 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-INF1xxx.
<h2 id="___sec28">The course has two central parts </h2>
<h2 id="___sec29">The course has two central parts </h2>
<ol>
<li> Statistical analysis and optimization of data</li>
<li> Machine learning</li>
</ol>
<h3 id="___sec29">Statistical analysis and optimization of data </h3>
<h3 id="___sec30">Statistical analysis and optimization of data </h3>
<p>
The following topics will be covered
@@ -926,7 +956,7 @@ The following topics will be covered
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
</ul>
<h3 id="___sec30">Machine learning </h3>
<h3 id="___sec31">Machine learning </h3>
<p>
The following topics will be covered
@@ -942,14 +972,14 @@ The following topics will be covered
All the above topics will be supported by examples, hands-on exercises and project work.
<h2 id="___sec31">Recommended textbooks </h2>
<h2 id="___sec32">Recommended textbooks </h2>
<ul>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_self">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a></li>
<li> <a href="http://shop.oreilly.com/product/0636920052289.do" target="_self">Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly</a></li>
</ul>
<h2 id="___sec32"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" target="_self">Other textbooks</a> </h2>
<h2 id="___sec33"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" target="_self">Other textbooks</a> </h2>
<p>
<b>General learning book on statistical analysis</b>: