diff --git a/doc/web/.course_html_file_collection b/doc/web/.course_html_file_collection deleted file mode 100644 index 70be3a80e..000000000 --- a/doc/web/.course_html_file_collection +++ /dev/null @@ -1 +0,0 @@ -course.html diff --git a/doc/web/course.dlog b/doc/web/course.dlog index 22bd72d60..cd441dd40 100644 --- a/doc/web/course.dlog +++ b/doc/web/course.dlog @@ -198,3 +198,6 @@ 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 4119329b3..23eb0c313 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -3,7 +3,7 @@ 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', ] +published = ['week34', 'week35', 'week36', 'week37', 'week38', 'week39', 'week40', 'week41', 'week42', 'week43', 'week44', 'week45', 'week46', 'week47', ] chapters = { '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, from ordinary Least Squares to Ridge and Lasso Regression', @@ -14,12 +14,11 @@ chapters = { 'week40': 'Week 40 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm', 'week41': 'Week 41 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow', 'week42': 'Week 42 October 18-22: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)', - 'week43': 'Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks', - 'week44': 'Week 44 November 1-5: Decision Trees and Bagging', - 'week45': 'Week 45 November 8-12: Random Forests and Gradient Boosting and Workshop on Project 3', + 'week43': 'Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis', + 'week44': 'Week 44 November 1-5: Principal Component analysis, Clustering and Decision Trees', + 'week45': 'Week 45 November 8-12: Decision Trees, 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', - 'week48': 'Week 48 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives', + 'week47': 'Week 47 November 22-26: Support Vector Machines and Summary of Course with Future Perspectives', } %> diff --git a/doc/web/course.html b/doc/web/course.html index feeb40811..18572782e 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -1,6 +1,7 @@ @@ -8,16 +9,9 @@ Automatically generated HTML file from DocOnce source - Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised) - - - - - - +
+

Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)

+
- - -

Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)

- -

-

Morten Hjorth-Jensen [1, 2]
- -

- -

[1] Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA
-
[2] Department of Physics and Center for Computing in Science Education (office FØ470), University of Oslo, Norway
+
+[1] Department of Physics and Astronomy and Facility for Rare ion Beams and National Superconducting Cyclotron Laboratory, Michigan State University, USA +
+
+[2] Department of Physics and Center for Computing in Science Education (office FØ470), University of Oslo, Norway +

-

-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. - +

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

-

Week 35 August 30- September 3: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression

-

Week 36 September 6-10: Statistical analysis and discussion of Ridge and Lasso regression

-

Week 37 September 13-17: Resampling techniques, Cross-validation and the Bootstrap

-

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

-

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

-

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

-

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

-

Week 42 October 18-22: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)

- -

Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks

+

Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis

- -

Week 44 November 1-5: Decision Trees and Bagging

+

Week 44 November 1-5: Principal Component analysis, Clustering and Decision Trees

- -

Week 45 November 8-12: Random Forests and Gradient Boosting and Workshop on Project 3

+

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

-

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

- -

Week 47 November 22-26: Support Vector Machines

+

Week 47 November 22-26: 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

- - - -

Textbook

- - -

Projects Fall 2021 (dates are tentative)

-

Project 1, Deadline October 11 (available September 10)

- -

Project 2, Deadline November 15 (available October 12)

- -

Project 3, Deadline December 13 (available November 12)

- - - - - - diff --git a/doc/web/tmp_mako__course.do.txt b/doc/web/tmp_mako__course.do.txt index bda730ff7..c2c7edfc5 100644 --- a/doc/web/tmp_mako__course.do.txt +++ b/doc/web/tmp_mako__course.do.txt @@ -123,7 +123,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week42/ipynb/week42.ipynb" -===== Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks ===== +===== Week 43 October 25-29: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis ===== @@ -135,7 +135,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week43/ipynb/week43.ipynb" -===== Week 44 November 1-5: Decision Trees and Bagging ===== +===== Week 44 November 1-5: Principal Component analysis, Clustering and Decision Trees ===== @@ -147,7 +147,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week44/ipynb/week44.ipynb" -===== Week 45 November 8-12: Random Forests and Gradient Boosting and Workshop on Project 3 ===== +===== Week 45 November 8-12: Decision Trees, Random Forests and Gradient Boosting ===== @@ -171,7 +171,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week46/ipynb/week46.ipynb" -===== Week 47 November 22-26: Support Vector Machines ===== +===== Week 47 November 22-26: Support Vector Machines and Summary of Course with Future Perspectives ===== @@ -183,18 +183,6 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week47/ipynb/week47.ipynb" -===== Week 48 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives ===== - - - - * HTML: - * "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48.html" - * "reveal.js beige slide style": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-reveal.html" - * "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/week48/html/week48-bs.html" - * Jupyter notebook: - * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week48/ipynb/week48.ipynb" - - !split ===== Textbook =====