diff --git a/doc/web/course.dlog b/doc/web/course.dlog index d06adc184..60f4a7773 100644 --- a/doc/web/course.dlog +++ b/doc/web/course.dlog @@ -183,3 +183,6 @@ Translating doconce text in tmp_mako__course.do.txt to 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 097606fb3..f3d5d4130 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -5,21 +5,21 @@ 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', 'week48', ] chapters = { - 'week34': 'Week 35 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression', - 'week35': 'Week 36 August 30- September 3: Linear regression and review of statistics and probability theory', - 'week36': 'Week 37 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression', - 'week37': 'Week 38 September 13-17: Ridge and Lasso Regression', - 'week38': 'Week 39 September 20-24: Summary of linear regression methods and start Logistic Regression', - 'week39': 'Week 40 September 27- October 1: Logistic Regression and Gradient methods', - 'week40': 'Week 41 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model', - 'week41': 'Week 42 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow', - 'week42': 'Week 43 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks', - 'week43': 'Week 44 October 25-29: Dimesionality Reduction, Principal Component analysis', - 'week44': 'Week 45 November 1-5: Decision Trees and Bagging', - 'week45': 'Week 46 November 8-12: Random Forests and Gradient Boosting', - 'week46': 'Week 47 November 15-19: Gradient boosting and Support Vector Machines', - 'week47': 'Week 48 November 22-26: Support Vector Machines and Workshop on Project 3', - 'week48': 'Week 49 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives', + 'week34': 'Week 34 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression', + 'week35': 'Week 35 August 30- September 3: Linear regression and review of statistics and probability theory', + 'week36': 'Week 36 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression', + 'week37': 'Week 37 September 13-17: Ridge and Lasso Regression', + 'week38': 'Week 38 September 20-24: Summary of linear regression methods and start Logistic Regression', + 'week39': 'Week 39 September 27- October 1: Logistic Regression and Gradient methods', + 'week40': 'Week 40 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model', + 'week41': 'Week 41 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow', + 'week42': 'Week 42 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks', + 'week43': 'Week 43 October 25-29: Dimesionality Reduction, Principal Component analysis', + 'week44': 'Week 44 November 1-5: Decision Trees and Bagging', + 'week45': 'Week 45 November 8-12: Random Forests and Gradient Boosting', + 'week46': 'Week 46 November 15-19: Gradient boosting and Support Vector Machines', + 'week47': 'Week 47 November 22-26: Support Vector Machines and Workshop on Project 3', + 'week48': 'Week 48 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives', } %> diff --git a/doc/web/course.html b/doc/web/course.html index a33a4cb4e..a8d2bdda8 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -43,79 +43,79 @@ div { text-align: justify; text-justify: inter-word; }

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. -

Week 35 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression

+

Week 34 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression

-

Week 36 August 30- September 3: Linear regression and review of statistics and probability theory

+

Week 35 August 30- September 3: Linear regression and review of statistics and probability theory

-

Week 37 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression

+

Week 36 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression

-

Week 38 September 13-17: Ridge and Lasso Regression

+

Week 37 September 13-17: Ridge and Lasso Regression

-

Week 39 September 20-24: Summary of linear regression methods and start Logistic Regression

+

Week 38 September 20-24: Summary of linear regression methods and start Logistic Regression

-

Week 40 September 27- October 1: Logistic Regression and Gradient methods

+

Week 39 September 27- October 1: Logistic Regression and Gradient methods

-

Week 41 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model

+

Week 40 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model

-

Week 42 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow

+

Week 41 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow

-

Week 43 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks

+

Week 42 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks

-

Week 44 October 25-29: Dimesionality Reduction, Principal Component analysis

+

Week 43 October 25-29: Dimesionality Reduction, Principal Component analysis

-

Week 45 November 1-5: Decision Trees and Bagging

+

Week 44 November 1-5: Decision Trees and Bagging

-

Week 46 November 8-12: Random Forests and Gradient Boosting

+

Week 45 November 8-12: Random Forests and Gradient Boosting

-

Week 47 November 15-19: Gradient boosting and Support Vector Machines

+

Week 46 November 15-19: Gradient boosting and Support Vector Machines

-

Week 48 November 22-26: Support Vector Machines and Workshop on Project 3

+

Week 47 November 22-26: Support Vector Machines and Workshop on Project 3

-

Week 49 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives

+

Week 48 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives