Morten Hjorth-Jensen
340 lines
11 KiB
HTML
340 lines
11 KiB
HTML
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<title>Overview of course material: Data Analysis and Machine Learning</title>
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'sections': [('Monte Carlo methods and elements of probability theory',
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2,
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None,
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'___sec0'),
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('Linear regression and beyond', 2, None, '___sec1'),
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('Elements of Bayesian theory', 2, None, '___sec2'),
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('Neural Networks', 2, None, '___sec3'),
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('New for Fall 2017: teach yourself C++', 2, None, '___sec4'),
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('Projects Fall 2017', 2, None, '___sec5'),
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('Project 1', 3, None, '___sec6'),
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('Basic Syllabus', 2, None, '___sec7'),
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('Additional literature', 2, None, '___sec8')]}
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<!-- ------------------- main content ---------------------- -->
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<!-- Strange way of testing for vortex... -->
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<center><h1>Overview of course material: Data Analysis and Machine Learning</h1></center> <!-- document title -->
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<p>
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<!-- author(s): <a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a> -->
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<center>
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<b><a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a></b> [1, 2]
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</center>
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<p>
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<!-- institution(s) -->
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<center>[1] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA</b></center>
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<center>[2] <b>Department of Physics, University of Oslo, Norway</b></center>
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<br>
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<p>
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The teaching material is produced in various formats for printing and on-screen reading.
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<p>
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<!-- !split -->
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<div class="alert alert-block alert-warning alert-text-normal">
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<b>Warning.</b>
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<p>
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The PDF files are based on LaTeX and have seldom technical
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failures that cannot be easily corrected.
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The HTML-based files, called "HTML" and "ipynb" below, apply MathJax
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for rendering LaTeX formulas and sometimes this technology gives rise
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to unexpected failures (e.g.,
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incorrect rendering in a web page despite correct LaTeX syntax in the
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formula). Consult the corresponding PDF
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files if you find missing or incorrectly rendered
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formulas in HTML or ipython notebook files.
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</div>
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<h2 id="___sec0">Monte Carlo methods and elements of probability theory </h2>
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<ul>
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<li> LaTeX PDF:</li>
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<ul>
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<li> For printing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/pdf/Statistics-minted.pdf" target="_self">Standard one-page format</a></li>
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</ul>
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<li> For screen viewing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/pdf/Statistics-beamer.pdf" target="_self">standard Beamer format</a></li>
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</ul>
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</ul>
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<li> HTML:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics.html" target="_self">Plain html</a></li>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-reveal.html" target="_self">reveal.js beige slide style</a></li>
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<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>
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</ul>
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<li> iPython notebook:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Statistics/ipynb/Statistics.ipynb" target="_self">ipynb file</a></li>
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</ul>
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</ul>
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<h2 id="___sec1">Linear regression and beyond </h2>
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<ul>
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<li> LaTeX PDF:</li>
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<ul>
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<li> For printing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/pdf/Regression-minted.pdf" target="_self">Standard one-page format</a></li>
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</ul>
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<li> For screen viewing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/pdf/Regression-beamer.pdf" target="_self">standard Beamer format</a></li>
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</ul>
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</ul>
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<li> HTML:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression.html" target="_self">Plain html</a></li>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-reveal.html" target="_self">reveal.js beige slide style</a></li>
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<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>
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</ul>
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<li> iPython notebook:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Regression/ipynb/Regression.ipynb" target="_self">ipynb file</a></li>
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</ul>
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</ul>
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<h2 id="___sec2">Elements of Bayesian theory </h2>
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<ul>
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<li> LaTeX PDF:</li>
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<ul>
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<li> For printing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/pdf/Bayesian-minted.pdf" target="_self">Standard one-page format</a></li>
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</ul>
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<li> For screen viewing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/pdf/Bayesian-beamer.pdf" target="_self">standard Beamer format</a></li>
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</ul>
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</ul>
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<li> HTML:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian.html" target="_self">Plain html</a></li>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-reveal.html" target="_self">reveal.js beige slide style</a></li>
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<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>
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</ul>
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<li> iPython notebook:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/ipynb/Bayesian.ipynb" target="_self">ipynb file</a></li>
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</ul>
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</ul>
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<h2 id="___sec3">Neural Networks </h2>
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<ul>
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<li> LaTeX PDF:</li>
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<ul>
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<li> For printing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/pdf/NeuralNet-minted.pdf" target="_self">Standard one-page format</a></li>
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</ul>
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<li> For screen viewing:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/pdf/NeuralNet-beamer.pdf" target="_self">standard Beamer format</a></li>
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</ul>
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</ul>
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<li> HTML:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet.html" target="_self">Plain html</a></li>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-reveal.html" target="_self">reveal.js beige slide style</a></li>
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<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>
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</ul>
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<li> iPython notebook:</li>
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<ul>
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<li> <a href="https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb" target="_self">ipynb file</a></li>
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</ul>
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</ul>
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<!-- !split -->
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<h2 id="___sec4">New for Fall 2017: teach yourself C++ </h2>
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<ul>
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<li> HTML format only:</li>
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<ul>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/pub/learningcpp/html/learningcpp-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
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</ul>
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</ul>
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<!-- !split -->
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<h2 id="___sec5">Projects Fall 2017 </h2>
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<h3 id="___sec6">Project 1 </h3>
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<ul>
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<li> LaTeX and PDF:</li>
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<ul>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/Projects/2017/Project1/pdf/Project1.tex" target="_self">LaTex file</a></li>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/Projects/2017/Project1/pdf/Project1.pdf" target="_self">PDF file</a></li>
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</ul>
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<li> HTML:</li>
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<ul>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/Projects/2017/Project1/html/Project1.html" target="_self">Plain html</a></li>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/Projects/2017/Project1/html/Project1-bs.html" target="_self">Bootstrap slide style, easy for reading on mobile devices</a></li>
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</ul>
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<li> iPython notebook:</li>
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<ul>
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<li> <a href="http://compphysics.github.io/ComputationalPhysics/doc/Projects/2017/Project1/ipynb/Project1.ipynb" target="_self">ipynb file</a></li>
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</ul>
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</ul>
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<h2 id="___sec7">Basic Syllabus </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b></b>
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<p>
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To be filled in
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</div>
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<h2 id="___sec8">Additional literature </h2>
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<div class="alert alert-block alert-block alert-text-normal">
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<b></b>
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<p>
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More to come
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</div>
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<p>
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<!-- ------------------- end of main content --------------- -->
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</body>
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</html>
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