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 name="slide_types(name)"> 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"