From 8b7b99310318f508ad2fb863294805945974d42a Mon Sep 17 00:00:00 2001 From: mhjensen Date: Wed, 16 Sep 2020 10:51:03 +0200 Subject: [PATCH] updating course schedule --- doc/web/course.dlog | 3 + doc/web/course.do.txt | 50 +-- doc/web/course.html | 590 +++++++++----------------------- doc/web/tmp_mako__course.do.txt | 279 +++++---------- 4 files changed, 271 insertions(+), 651 deletions(-) diff --git a/doc/web/course.dlog b/doc/web/course.dlog index d5be0bbdf..0da0f1e9a 100644 --- a/doc/web/course.dlog +++ b/doc/web/course.dlog @@ -135,3 +135,6 @@ output in course.html running mako on course.do.txt to make tmp_mako__course.do.txt translating doconce text in tmp_mako__course.do.txt to html output in course.html +running mako on course.do.txt to make tmp_mako__course.do.txt +translating doconce text in tmp_mako__course.do.txt to html +output in course.html diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt index 022e60e8a..363c038b5 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -1,30 +1,26 @@ -TITLE: Overview of course material: Data Analysis and Machine Learning +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 (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',] +published = ['week34', 'week35', 'week36', 'week37', 'week38', 'week39', 'week40', 'week41', 'week42', 'week43', 'week44', 'week45', 'week46', 'week47', 'week48', ] 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', + 'week34': 'Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression', + 'week35': 'Week35 August 24-28: Linear regression and review of statistics and probability theory', + 'week36': 'Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression', + 'week37': 'Week37 September 7-11: Ridge and Lasso Regression', + 'week38': 'Week38 September 14-18: Summary of linear regression methods and start Logistic Regression', + 'week39': 'Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks', + 'week40': 'Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model', + 'week41': 'Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)', + 'week42': 'Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks', + 'week43': 'Week43 October 19-23: Dimesionality Reduction, Principal Component analysis', + 'week44': 'Week44 October 26-30: Decision Trees and Bagging', + 'week45': 'Week45 November 2-6: Random Forests and Gradient Boosting', + 'week46': 'Week46 November 9-13: Support Vector Machines', + 'week47': 'Week47 November 16-20: Support Vector Machines', + 'week48': 'Week48 November 23-27: Unsupervised learning, clustering and summary of course', + } %> @@ -34,9 +30,6 @@ chapters = { <%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" @@ -96,6 +89,13 @@ ${text_types(ch)} * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2020/hw2/html/hw2-bs.html" +=== 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" + + === Project 1, Deadline October 5 (available September 1) === * LaTeX and PDF: diff --git a/doc/web/course.html b/doc/web/course.html index 33723d237..ccbe52fab 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Overview of course material: Data Analysis and Machine Learning +Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)