diff --git a/doc/web/course.dlog b/doc/web/course.dlog index 92aadb3cb..fe157228c 100644 --- a/doc/web/course.dlog +++ b/doc/web/course.dlog @@ -249,3 +249,6 @@ 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 f3f96caf3..2895b92b0 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -5,20 +5,20 @@ AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Depart 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 21-25:Basic introduction to the course with schedule etc and start Linear Regression', - 'week35': 'Week 35 August 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics', - 'week36': 'Week 36 September 4-8: Statistical analysis and discussion of Ridge and Lasso regression', - 'week37': 'Week 37 September 11-15: Resampling techniques, Cross-validation and the Bootstrap', - 'week38': 'Week 38 September 18-22: Summary of linear regression methods and start Logistic Regression', - 'week39': 'Week 39 September 25-29: Logistic Regression and Gradient methods', + 'week34': 'Week 34 August 19-23:Basic introduction to the course with schedule etc and start Linear Regression', + 'week35': 'Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics', + 'week36': 'Week 36 September 2-6: Statistical analysis and discussion of Ridge and Lasso regression', + 'week37': 'Week 37 September 9-13: Resampling techniques, Cross-validation and the Bootstrap', + 'week38': 'Week 38 September 16-20: Summary of linear regression methods and start Logistic Regression', + 'week39': 'Week 39 September 23-27: Logistic Regression and Gradient methods', 'week40': 'Week 40 October 2-6: Stochastic Gradient Descent', - 'week41': 'Week 41 October 9-13: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm', - 'week42': 'Week 42 October 16-20: Building a multi-layer perceptron code and introduction to Tensorflow', - 'week43': 'Week 43 October 23-27: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)', - 'week44': 'Week 44 October 30- November 3: Convolutional Neural Networks', - 'week45': 'Week 45 November 6-10: Convolutional neural networks and Recurrent neural networks', - 'week46': 'Week 46 November 13-17: Decision trees, random forests and other ensemble methods', - 'week47': 'Week 47 November 20-24: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives', + 'week41': 'Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm', + 'week42': 'Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow', + 'week43': 'Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)', + 'week44': 'Week 44 October 28- November 1: Convolutional Neural Networks', + 'week45': 'Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks', + 'week46': 'Week 46 November 11-15: Decision trees, random forests and other ensemble methods', + 'week47': 'Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives', } %> diff --git a/doc/web/course.html b/doc/web/course.html index e8dc6040c..79e9f70da 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -117,75 +117,74 @@ div.toc p,a {

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.

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Week 34 August 21-25:Basic introduction to the course with schedule etc and start Linear Regression

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Week 34 August 19-23:Basic introduction to the course with schedule etc and start Linear Regression

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Week 35 August 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics

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Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics

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Week 36 September 4-8: Statistical analysis and discussion of Ridge and Lasso regression

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Week 36 September 2-6: Statistical analysis and discussion of Ridge and Lasso regression

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Week 37 September 11-15: Resampling techniques, Cross-validation and the Bootstrap

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Week 37 September 9-13: Resampling techniques, Cross-validation and the Bootstrap

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Week 38 September 18-22: Summary of linear regression methods and start Logistic Regression

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Week 38 September 16-20: Summary of linear regression methods and start Logistic Regression

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Week 39 September 25-29: Logistic Regression and Gradient methods

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Week 39 September 23-27: Logistic Regression and Gradient methods

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Week 41 October 9-13: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

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Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm

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Week 42 October 16-20: Building a multi-layer perceptron code and introduction to Tensorflow

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Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow

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Week 43 October 23-27: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

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Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

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Week 44 October 30- November 3: Convolutional Neural Networks

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Week 44 October 28- November 1: Convolutional Neural Networks

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Week 45 November 6-10: Convolutional neural networks and Recurrent neural networks

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Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks

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Week 46 November 13-17: Decision trees, random forests and other ensemble methods

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Week 46 November 11-15: Decision trees, random forests and other ensemble methods

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Week 47 November 20-24: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives

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Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives