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added dim red material

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mhjensen
2018-10-24 08:33:06 +02:00
parent ec7b073679
commit d2efb8dd0b
7 changed files with 1072 additions and 44 deletions
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doc/web/course.html
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@@ -82,44 +82,45 @@ div { text-align: justify; text-justify: inter-word; }
2,
None,
'___sec4'),
('Splines and Gradient methods', 2, None, '___sec5'),
('Gradient methods', 2, None, '___sec5'),
('Regression Methods', 2, None, '___sec6'),
('Logistic Regression', 2, None, '___sec7'),
('Neural Networks', 2, None, '___sec8'),
('Elements of Bayesian theory', 2, None, '___sec9'),
('Reduction of dimensionality', 2, None, '___sec9'),
('Elements of Bayesian theory', 2, None, '___sec10'),
('Decision trees, from simple to random ones',
2,
None,
'___sec10'),
('Support Vector Machines', 2, None, '___sec11'),
'___sec11'),
('Support Vector Machines', 2, None, '___sec12'),
('Unsupervised Learning, Boltzmann Machines',
2,
None,
'___sec12'),
('Python and Scikit Learn, a short guide', 2, None, '___sec13'),
('Teach yourself C++', 2, None, '___sec14'),
('Projects and Exercises Fall 2018', 2, None, '___sec15'),
('First homework set, week 35', 3, None, '___sec16'),
('Second homework set, week 36', 3, None, '___sec17'),
('Project 1, Deadline October 1', 3, None, '___sec18'),
('Project 2, Deadline November 5', 3, None, '___sec19'),
('Project 3, Deadline November 30', 3, None, '___sec20'),
('Course content', 3, None, '___sec21'),
('Learning outcomes', 2, None, '___sec22'),
('Prerequisites', 2, None, '___sec23'),
('The course has two central parts', 2, None, '___sec24'),
'___sec13'),
('Python and Scikit Learn, a short guide', 2, None, '___sec14'),
('Teach yourself C++', 2, None, '___sec15'),
('Projects and Exercises Fall 2018', 2, None, '___sec16'),
('First homework set, week 35', 3, None, '___sec17'),
('Second homework set, week 36', 3, None, '___sec18'),
('Project 1, Deadline October 1', 3, None, '___sec19'),
('Project 2, Deadline November 5', 3, None, '___sec20'),
('Project 3, Deadline November 30', 3, None, '___sec21'),
('Course content', 3, None, '___sec22'),
('Learning outcomes', 2, None, '___sec23'),
('Prerequisites', 2, None, '___sec24'),
('The course has two central parts', 2, None, '___sec25'),
('Statistical analysis and optimization of data',
3,
None,
'___sec25'),
('Machine learning', 3, None, '___sec26'),
('Recommended textbooks', 2, None, '___sec27'),
'___sec26'),
('Machine learning', 3, None, '___sec27'),
('Recommended textbooks', 2, None, '___sec28'),
('"Other '
'textbooks":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks"',
2,
None,
'___sec28'),
('Teaching schedule Fall 2018', 2, None, '___sec29')]}
'___sec29'),
('Teaching schedule Fall 2018', 2, None, '___sec30')]}
end of tocinfo -->
<body>
@@ -327,7 +328,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec5">Splines and Gradient methods </h2>
<h2 id="___sec5">Gradient methods </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -443,7 +444,36 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec9">Elements of Bayesian theory </h2>
<h2 id="___sec9">Reduction of dimensionality </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-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>
</ul>
</ul>
<h2 id="___sec10">Elements of Bayesian theory </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -472,7 +502,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec10">Decision trees, from simple to random ones </h2>
<h2 id="___sec11">Decision trees, from simple to random ones </h2>
<ul>
<li> LaTeX PDF:</li>
@@ -501,7 +531,7 @@ 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>
@@ -530,7 +560,7 @@ 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>
@@ -561,7 +591,7 @@ formulas in HTML or ipython notebook files.
<!-- !split -->
<h2 id="___sec13">Python and Scikit Learn, a short guide </h2>
<h2 id="___sec14">Python and Scikit Learn, a short guide </h2>
<ul>
<li> HTML format only:</li>
@@ -574,7 +604,7 @@ formulas in HTML or ipython notebook files.
<!-- !split -->
<h2 id="___sec14">Teach yourself C++ </h2>
<h2 id="___sec15">Teach yourself C++ </h2>
<ul>
<li> HTML format only:</li>
@@ -587,9 +617,9 @@ formulas in HTML or ipython notebook files.
<!-- !split -->
<h2 id="___sec15">Projects and Exercises Fall 2018 </h2>
<h2 id="___sec16">Projects and Exercises Fall 2018 </h2>
<h3 id="___sec16">First homework set, week 35 </h3>
<h3 id="___sec17">First homework set, week 35 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -608,7 +638,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec17">Second homework set, week 36 </h3>
<h3 id="___sec18">Second homework set, week 36 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -627,7 +657,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec18">Project 1, Deadline October 1 </h3>
<h3 id="___sec19">Project 1, Deadline October 1 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -646,7 +676,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec19">Project 2, Deadline November 5 </h3>
<h3 id="___sec20">Project 2, Deadline November 5 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -665,7 +695,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec20">Project 3, Deadline November 30 </h3>
<h3 id="___sec21">Project 3, Deadline November 30 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -684,7 +714,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h3 id="___sec21">Course content </h3>
<h3 id="___sec22">Course content </h3>
<p>
Probability theory and statistical methods play a central role in science. Nowadays we are
@@ -703,7 +733,7 @@ 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="___sec22">Learning outcomes </h2>
<h2 id="___sec23">Learning outcomes </h2>
<p>
The course introduces a variety of central algorithms and methods
@@ -720,19 +750,19 @@ 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="___sec23">Prerequisites </h2>
<h2 id="___sec24">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="___sec24">The course has two central parts </h2>
<h2 id="___sec25">The course has two central parts </h2>
<ol>
<li> Statistical analysis and optimization of data</li>
<li> Machine learning</li>
</ol>
<h3 id="___sec25">Statistical analysis and optimization of data </h3>
<h3 id="___sec26">Statistical analysis and optimization of data </h3>
<p>
The following topics will be covered
@@ -749,7 +779,7 @@ The following topics will be covered
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
</ul>
<h3 id="___sec26">Machine learning </h3>
<h3 id="___sec27">Machine learning </h3>
<p>
The following topics will be covered
@@ -765,14 +795,14 @@ The following topics will be covered
All the above topics will be supported by examples, hands-on exercises and project work.
<h2 id="___sec27">Recommended textbooks </h2>
<h2 id="___sec28">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="___sec28"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" target="_self">Other textbooks</a> </h2>
<h2 id="___sec29"><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>:
@@ -794,7 +824,7 @@ All the above topics will be supported by examples, hands-on exercises and proje
<!-- !split -->
<h2 id="___sec29">Teaching schedule Fall 2018 </h2>
<h2 id="___sec30">Teaching schedule Fall 2018 </h2>
<p>
Acronyms for textbooks and references to chapter