From b53b14b402f46833ea12e4176a5f5515265a0895 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Tue, 23 Aug 2022 10:29:51 +0200 Subject: [PATCH] update of course overview --- doc/web/course.dlog | 3 + doc/web/course.do.txt | 66 +++++++-------- doc/web/course.html | 142 ++++++++++++++++---------------- doc/web/tmp_mako__course.do.txt | 66 +++++++-------- 4 files changed, 140 insertions(+), 137 deletions(-) diff --git a/doc/web/course.dlog b/doc/web/course.dlog index ef9845192..8ebfb5b42 100644 --- a/doc/web/course.dlog +++ b/doc/web/course.dlog @@ -204,3 +204,6 @@ 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 a2e132e76..db623c225 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 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', - 'week36': 'Week 36 September 6-10: Statistical analysis and discussion of Ridge and Lasso regression', - 'week37': 'Week 37 September 13-17: Resampling techniques, Cross-validation and the Bootstrap', - '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, 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, 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 and Summary of Course with Future Perspectives', + 'week34': 'Week 34 August 22-26:Basic introduction to the course with schedule etc and start Linear Regression', + 'week35': 'Week 35 August 29- September 2: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression', + 'week36': 'Week 36 September 5-9: Statistical analysis and discussion of Ridge and Lasso regression', + 'week37': 'Week 37 September 12-16: Resampling techniques, Cross-validation and the Bootstrap', + 'week38': 'Week 38 September 19-23: Summary of linear regression methods and start Logistic Regression', + 'week39': 'Week 39 September 26-30: Logistic Regression and Gradient methods', + 'week40': 'Week 40 October 3-7: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm', + 'week41': 'Week 41 October 10-14: Building a multi-layer perceptron code and introduction to Tensorflow', + 'week42': 'Week 42 October 17-21: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)', + 'week43': 'Week 43 October 24-28: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis', + 'week44': 'Week 44 October 31- November 4: Principal Component analysis, Clustering and Decision Trees', + 'week45': 'Week 45 November 7-11: Decision Trees, Random Forests and Gradient Boosting', + 'week46': 'Week 46 November 14-18: Gradient boosting and Support Vector Machines', + 'week47': 'Week 47 November 21-25: Support Vector Machines and Summary of Course with Future Perspectives', } %> @@ -63,39 +63,39 @@ ${text_types(ch)} !split -===== Projects Fall 2021 (dates are tentative) ===== +===== Projects Fall 2022 (dates are tentative) ===== -=== Project 1, Deadline October 11 (available September 10) === +=== Project 1, Deadline October 7 (available September 3) === * LaTeX and PDF: - * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/pdf/Project1.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/pdf/Project1.pdf" + * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/html/Project1.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/html/Project1-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/ipynb/Project1.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/ipynb/Project1.ipynb" -=== Project 2, Deadline November 20 (available October 12) === +=== Project 2, Deadline November 11 (available October 7) === * LaTeX and PDF: - * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/pdf/Project2.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/pdf/Project2.pdf" + * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/html/Project2.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/html/Project2-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/ipynb/Project2.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/ipynb/Project2.ipynb" -=== Project 3, Deadline December 17 (available November 13) === +=== Project 3, Deadline December 9 (available November 11) === * LaTeX and PDF: - * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/pdf/Project3.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/pdf/Project3.pdf" + * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/html/Project3.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/html/Project3-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/ipynb/Project3.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/ipynb/Project3.ipynb" diff --git a/doc/web/course.html b/doc/web/course.html index a7b15a37a..99e7e2c3e 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -117,95 +117,95 @@ div.toc p,a { @@ -228,7 +228,7 @@ end of tocinfo -->

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 34 August 22-26: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 35 August 29- September 2: 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 36 September 5-9: Statistical analysis and discussion of Ridge and Lasso regression

-

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

+

Week 37 September 12-16: Resampling techniques, Cross-validation and the Bootstrap

-

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

+

Week 38 September 19-23: Summary of linear regression methods and start Logistic Regression

-

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

+

Week 39 September 26-30: 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 40 October 3-7: 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 41 October 10-14: 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 42 October 17-21: 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, Principal Component Analysis

+

Week 43 October 24-28: Deep learning, Convolutional Neural Networks and Recurrent Neural Networks, Principal Component Analysis

-

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

+

Week 44 October 31- November 4: Principal Component analysis, Clustering and Decision Trees

-

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

+

Week 45 November 7-11: Decision Trees, Random Forests and Gradient Boosting

-

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

+

Week 46 November 14-18: Gradient boosting and Support Vector Machines

-

Week 47 November 22-26: Support Vector Machines and Summary of Course with Future Perspectives

+

Week 47 November 21-25: Support Vector Machines and Summary of Course with Future Perspectives

-

Projects Fall 2021 (dates are tentative)

-

Project 1, Deadline October 11 (available September 10)

+

Projects Fall 2022 (dates are tentative)

+

Project 1, Deadline October 7 (available September 3)

-

Project 2, Deadline November 20 (available October 12)

+

Project 2, Deadline November 11 (available October 7)

-

Project 3, Deadline December 17 (available November 13)

+

Project 3, Deadline December 9 (available November 11)

diff --git a/doc/web/tmp_mako__course.do.txt b/doc/web/tmp_mako__course.do.txt index 2a1238eae..afb8cd481 100644 --- a/doc/web/tmp_mako__course.do.txt +++ b/doc/web/tmp_mako__course.do.txt @@ -15,7 +15,7 @@ The teaching material is produced in various formats for running codes (jupyter -===== Week 34 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression ===== +===== Week 34 August 22-26:Basic introduction to the course with schedule etc and start Linear Regression ===== @@ -27,7 +27,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week34/ipynb/week34.ipynb" -===== Week 35 August 30- September 3: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression ===== +===== Week 35 August 29- September 2: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression ===== @@ -39,7 +39,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week35/ipynb/week35.ipynb" -===== Week 36 September 6-10: Statistical analysis and discussion of Ridge and Lasso regression ===== +===== Week 36 September 5-9: Statistical analysis and discussion of Ridge and Lasso regression ===== @@ -51,7 +51,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week36/ipynb/week36.ipynb" -===== Week 37 September 13-17: Resampling techniques, Cross-validation and the Bootstrap ===== +===== Week 37 September 12-16: Resampling techniques, Cross-validation and the Bootstrap ===== @@ -63,7 +63,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week37/ipynb/week37.ipynb" -===== Week 38 September 20-24: Summary of linear regression methods and start Logistic Regression ===== +===== Week 38 September 19-23: Summary of linear regression methods and start Logistic Regression ===== @@ -75,7 +75,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week38/ipynb/week38.ipynb" -===== Week 39 September 27- October 1: Logistic Regression and Gradient methods ===== +===== Week 39 September 26-30: Logistic Regression and Gradient methods ===== @@ -87,7 +87,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week39/ipynb/week39.ipynb" -===== Week 40 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm ===== +===== Week 40 October 3-7: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm ===== @@ -99,7 +99,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week40/ipynb/week40.ipynb" -===== Week 41 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow ===== +===== Week 41 October 10-14: Building a multi-layer perceptron code and introduction to Tensorflow ===== @@ -111,7 +111,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week41/ipynb/week41.ipynb" -===== Week 42 October 18-22: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks) ===== +===== Week 42 October 17-21: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks) ===== @@ -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, Principal Component Analysis ===== +===== Week 43 October 24-28: 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: Principal Component analysis, Clustering and Decision Trees ===== +===== Week 44 October 31- November 4: 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: Decision Trees, Random Forests and Gradient Boosting ===== +===== Week 45 November 7-11: Decision Trees, Random Forests and Gradient Boosting ===== @@ -159,7 +159,7 @@ The teaching material is produced in various formats for running codes (jupyter * "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week45/ipynb/week45.ipynb" -===== Week 46 November 15-19: Gradient boosting and Support Vector Machines ===== +===== Week 46 November 14-18: Gradient boosting and Support Vector Machines ===== @@ -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 and Summary of Course with Future Perspectives ===== +===== Week 47 November 21-25: Support Vector Machines and Summary of Course with Future Perspectives ===== @@ -196,39 +196,39 @@ The teaching material is produced in various formats for running codes (jupyter !split -===== Projects Fall 2021 (dates are tentative) ===== +===== Projects Fall 2022 (dates are tentative) ===== -=== Project 1, Deadline October 11 (available September 10) === +=== Project 1, Deadline October 7 (available September 3) === * LaTeX and PDF: - * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/pdf/Project1.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/pdf/Project1.pdf" + * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/pdf/Project1.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/html/Project1.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/html/Project1-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/html/Project1-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project1/ipynb/Project1.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project1/ipynb/Project1.ipynb" -=== Project 2, Deadline November 20 (available October 12) === +=== Project 2, Deadline November 11 (available October 7) === * LaTeX and PDF: - * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/pdf/Project2.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/pdf/Project2.pdf" + * "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/pdf/Project2.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/html/Project2.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/html/Project2-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/html/Project2-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project2/ipynb/Project2.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project2/ipynb/Project2.ipynb" -=== Project 3, Deadline December 17 (available November 13) === +=== Project 3, Deadline December 9 (available November 11) === * LaTeX and PDF: - * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/pdf/Project3.tex" - * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/pdf/Project3.pdf" + * "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.tex" + * "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/pdf/Project3.pdf" * HTML: - * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/html/Project3.html" - * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/html/Project3-bs.html" + * "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3.html" + * "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/html/Project3-bs.html" * Jupyter notebook: - * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2021/Project3/ipynb/Project3.ipynb" + * "ipynb file": "http://compphysics.github.io/MachineLearning/doc/Projects/2022/Project3/ipynb/Project3.ipynb"