FYS-STK4155/doc/web/course.do.txt

TITLE: Overview of course material: Data Analysis and Machine Learning
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',]
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',
}
%>





<%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/${name}-reveal.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "${pub_url}/${name}/html/${name}-bs.html"
 * Jupyter notebook:
   * "ipynb file": "${pub_url}/${name}/ipynb/${name}.ipynb"
</%def>

<%def name="slide_types(name)">
</%def>

The teaching material is produced in various formats for printing and on-screen reading.

!split
!bwarning
The PDF files are based on LaTeX and have seldom technical
failures that cannot be easily corrected.
The HTML-based files, called ``HTML'' and ``ipynb'' below, apply MathJax
for rendering LaTeX formulas and sometimes this technology gives rise
to unexpected failures (e.g.,
incorrect rendering in a web page despite correct LaTeX syntax in the
formula). Consult the corresponding PDF
files if you find missing or incorrectly rendered
formulas in HTML or ipython notebook files.
!ewarning




% for ch in published:
===== ${chapters[ch]} =====

${text_types(ch)}

% endfor




!split
===== Projects and Exercises Fall 2020 =====

=== First homework set, week 35  ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw1/pdf/hw1.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw1/pdf/hw1.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw1/html/hw1.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw1/html/hw1-bs.html"

=== Second homework set, week 36  ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/pdf/hw2.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/pdf/hw2.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/html/hw2.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/html/hw2-bs.html"



=== Project 1, Deadline October 5 (available September 1) ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project1/pdf/Project1.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project1/pdf/Project1.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project1/html/Project1.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project1/html/Project1-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project1/ipynb/Project1.ipynb"

=== Project 2, Deadline November 2 (available September 28) ===
 * LaTeX and PDF:
     * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project2/pdf/Project2.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project2/pdf/Project2.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project2/html/Project2.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project2/html/Project2-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project2/ipynb/Project2.ipynb"


=== Project 3, Deadline December 7 (available November 2) ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project3/pdf/Project3.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project3/pdf/Project3.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project3/html/Project3.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project3/html/Project3-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/Project3/ipynb/Project3.ipynb"