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 and Center for Computing in Science Education (office FØ470), University of Oslo, Norway

<%
pub_url = 'https://compphysics.github.io/MachineLearning/doc/pub'
published = ['week34', 'week35', 'week36', 'week37', 'week38', 'week39', 'week40', 'week41', 'week42', 'week43', 'week44', 'week45', 'week46', 'week47', ]
chapters = {
 'week34': 'Week 34 August 22-26:Basic introduction to the course with schedule etc and start Linear Regression',
 'week35': 'Week 35 August 29- September 2: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics',
 'week36': 'Week 36 September 5-9: Statistical analysis and discussion of Ridge and Lasso regression',
 'week37': 'Week 37 September 12-16: Resampling techniques, Cross-validation and the Bootstrap',
 'week38': 'Week 38 September 19-23: Summary of linear regression methods and start Logistic Regression',
 'week39': 'Week 39 September 26-30: Logistic Regression and Gradient methods',
 'week40': 'Week 40 October 3-7: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm',
 'week41': 'Week 41 October 10-14: Building a multi-layer perceptron code and introduction to Tensorflow',
 'week42': 'Week 42 October 17-21: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)',
 'week43': 'Week 43 October 24-28: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis',
 'week44': 'Week 44 October 31- November 4: Principal Component analysis, Clustering and Decision Trees',
 'week45': 'Week 45 November 7-11: Decision Trees, Random Forests and Gradient Boosting',
 'week46': 'Week 46 November 14-18: Gradient boosting and Support Vector Machines',
 'week47': 'Week 47 November 21-25: Support Vector Machines and Summary of Course with Future Perspectives',

}
%>





<%def name="text_types(name)">

 * 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 running codes (jupyter notebooks) and on-screen reading. Below you will also find a link to the lecture notes as a textbook in PDF format and as a jupyter notebook as well. Projects and exercise sets are also included.



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

${text_types(ch)}

% endfor

!split
=====  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"





!split
===== Projects Fall 2022 (dates are tentative) =====


=== Project 1, Deadline October 7 (available September 3) ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/ipynb/Project1.ipynb"

=== Project 2, Deadline November 11 (available October 7) ===
 * LaTeX and PDF:
     * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/ipynb/Project2.ipynb"


=== Project 3, Deadline December 9 (available November 11) ===
 * LaTeX and PDF:
     * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.tex"
     * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.pdf"
 * HTML:
   * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3.html"
   * "Bootstrap  slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3-bs.html"
 * Jupyter notebook:
   * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/ipynb/Project3.ipynb"