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Morten Hjorth-Jensen
2021-08-22 23:30:24 +02:00
parent be71a16b8b
commit 7253396bc5
4 changed files with 111 additions and 100 deletions
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doc/web/course.dlog
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@@ -171,3 +171,6 @@ 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
doc/web/course.do.txt
+16 -16
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@@ -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 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression',
'week35': 'Week 35 August 24-28: Linear regression and review of statistics and probability theory',
'week36': 'Week 36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression',
'week37': 'Week 37 September 7-11: Ridge and Lasso Regression',
'week38': 'Week 38 September 14-18: Summary of linear regression methods and start Logistic Regression',
'week39': 'Week 39 September 21-25: Logistic Regression and Gradient methods',
'week40': 'Week 40 September 28 - October 2: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model',
'week41': 'Week 41 October 5-9: Building a multi-layer perceptron code and introduction to Tensorflow',
'week42': 'Week 42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks',
'week43': 'Week 43 October 19-23: Dimesionality Reduction, Principal Component analysis',
'week44': 'Week 44 October 26-30: Decision Trees and Bagging',
'week45': 'Week 45 November 2-6: Random Forests and Gradient Boosting',
'week46': 'Week 46 November 9-13: Gradient boosting and Support Vector Machines',
'week47': 'Week 47 November 16-20: Support Vector Machines and Workshop on Project 3',
'week48': 'Week 48 November 23-27: Support Vector Machines and Summary of Course with Future Perspectives',
'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',
}
%>
@@ -64,7 +64,7 @@ ${text_types(ch)}
!split
===== Projects and Exercises Fall 2020 =====
===== Projects and Exercises Fall 2021, to be updated =====
=== First homework set, week 35 ===
* LaTeX and PDF:
doc/web/course.html
+76 -68
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@@ -1,11 +1,11 @@
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<meta name="description" content="Overview of course material: Data Analysis and Machine Learning (weekly schedule may be revised)">
@@ -15,8 +15,8 @@ Automatically generated HTML file from DocOnce source
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@@ -43,96 +43,104 @@ div { text-align: justify; text-justify: inter-word; }
<!-- tocinfo
{'highest level': 2,
'sections': [('Week 34 August 17-21:Basic introduction to the course with '
'sections': [('Week 35 August 23-27:Basic introduction to the course with '
'schedule etc and start Linear Regression',
2,
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('Week 39 September 21-25: Logistic Regression and Gradient '
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('Week 40 September 28 - October 2: Stochastic Gradient Descent '
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('Week 41 October 4-8: Stochastic Gradient Descent and Neural '
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2,
None,
'___sec6'),
('Week 41 October 5-9: Building a multi-layer perceptron code '
'week-41-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',
2,
None,
'___sec7'),
('Week 42 October 12-16: Deep learning (Convolutional Neural '
'week-42-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',
2,
None,
'___sec8'),
('Week 43 October 19-23: Dimesionality Reduction, Principal '
'week-43-october-18-22-deep-learning-convolutional-neural-networks-and-recurrent-neural-networks'),
('Week 44 October 25-29: Dimesionality Reduction, Principal '
'Component analysis',
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None,
'___sec9'),
('Week 44 October 26-30: Decision Trees and Bagging',
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('Week 45 November 1-5: Decision Trees and Bagging',
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('Week 45 November 2-6: Random Forests and Gradient Boosting',
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('Week 46 November 8-12: Random Forests and Gradient Boosting',
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'___sec11'),
('Week 46 November 9-13: Gradient boosting and Support Vector '
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('Week 47 November 15-19: Gradient boosting and Support Vector '
'Machines',
2,
None,
'___sec12'),
('Week 47 November 16-20: Support Vector Machines and Workshop '
'week-47-november-15-19-gradient-boosting-and-support-vector-machines'),
('Week 48 November 22-26: Support Vector Machines and Workshop '
'on Project 3',
2,
None,
'___sec13'),
('Week 48 November 23-27: Support Vector Machines and Summary of '
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'week-48-november-22-26-support-vector-machines-and-workshop-on-project-3'),
('Week 49 November 29- December 3: Support Vector Machines and '
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2,
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('Textbook', 2, None, '___sec15'),
('Projects and Exercises Fall 2020', 2, None, '___sec16'),
('First homework set, week 35', 3, None, '___sec17'),
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'project-3-deadline-december-16-available-november-9')]}
end of tocinfo -->
<body>
@@ -161,7 +169,7 @@ end of tocinfo -->
<p>
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.
<h2 id="___sec0">Week 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression </h2>
<h2 id="week-35-august-23-27-basic-introduction-to-the-course-with-schedule-etc-and-start-linear-regression">Week 35 August 23-27:Basic introduction to the course with schedule etc and start Linear Regression </h2>
<ul>
<li> HTML:</li>
@@ -180,7 +188,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec1">Week 35 August 24-28: Linear regression and review of statistics and probability theory </h2>
<h2 id="week-36-august-30-september-3-linear-regression-and-review-of-statistics-and-probability-theory">Week 36 August 30- September 3: Linear regression and review of statistics and probability theory </h2>
<ul>
<li> HTML:</li>
@@ -199,7 +207,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec2">Week 36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression </h2>
<h2 id="week-37-september-6-10-resampling-techniques-cross-validation-and-bootstrap-and-start-discussion-of-ridge-regression">Week 37 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression </h2>
<ul>
<li> HTML:</li>
@@ -218,7 +226,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec3">Week 37 September 7-11: Ridge and Lasso Regression </h2>
<h2 id="week-38-september-13-17-ridge-and-lasso-regression">Week 38 September 13-17: Ridge and Lasso Regression </h2>
<ul>
<li> HTML:</li>
@@ -237,7 +245,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec4">Week 38 September 14-18: Summary of linear regression methods and start Logistic Regression </h2>
<h2 id="week-39-september-20-24-summary-of-linear-regression-methods-and-start-logistic-regression">Week 39 September 20-24: Summary of linear regression methods and start Logistic Regression </h2>
<ul>
<li> HTML:</li>
@@ -256,7 +264,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec5">Week 39 September 21-25: Logistic Regression and Gradient methods </h2>
<h2 id="week-40-september-27-october-1-logistic-regression-and-gradient-methods">Week 40 September 27- October 1: Logistic Regression and Gradient methods </h2>
<ul>
<li> HTML:</li>
@@ -275,7 +283,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec6">Week 40 September 28 - October 2: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model </h2>
<h2 id="week-41-october-4-8-stochastic-gradient-descent-and-neural-networks-starting-to-build-a-multi-layer-perceptron-model">Week 41 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model </h2>
<ul>
<li> HTML:</li>
@@ -294,7 +302,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec7">Week 41 October 5-9: Building a multi-layer perceptron code and introduction to Tensorflow </h2>
<h2 id="week-42-october-11-15-building-a-multi-layer-perceptron-code-and-introduction-to-tensorflow">Week 42 October 11-15: Building a multi-layer perceptron code and introduction to Tensorflow </h2>
<ul>
<li> HTML:</li>
@@ -313,7 +321,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec8">Week 42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks </h2>
<h2 id="week-43-october-18-22-deep-learning-convolutional-neural-networks-and-recurrent-neural-networks">Week 43 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks </h2>
<ul>
<li> HTML:</li>
@@ -332,7 +340,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec9">Week 43 October 19-23: Dimesionality Reduction, Principal Component analysis </h2>
<h2 id="week-44-october-25-29-dimesionality-reduction-principal-component-analysis">Week 44 October 25-29: Dimesionality Reduction, Principal Component analysis </h2>
<ul>
<li> HTML:</li>
@@ -351,7 +359,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec10">Week 44 October 26-30: Decision Trees and Bagging </h2>
<h2 id="week-45-november-1-5-decision-trees-and-bagging">Week 45 November 1-5: Decision Trees and Bagging </h2>
<ul>
<li> HTML:</li>
@@ -370,7 +378,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec11">Week 45 November 2-6: Random Forests and Gradient Boosting </h2>
<h2 id="week-46-november-8-12-random-forests-and-gradient-boosting">Week 46 November 8-12: Random Forests and Gradient Boosting </h2>
<ul>
<li> HTML:</li>
@@ -389,7 +397,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec12">Week 46 November 9-13: Gradient boosting and Support Vector Machines </h2>
<h2 id="week-47-november-15-19-gradient-boosting-and-support-vector-machines">Week 47 November 15-19: Gradient boosting and Support Vector Machines </h2>
<ul>
<li> HTML:</li>
@@ -408,7 +416,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec13">Week 47 November 16-20: Support Vector Machines and Workshop on Project 3 </h2>
<h2 id="week-48-november-22-26-support-vector-machines-and-workshop-on-project-3">Week 48 November 22-26: Support Vector Machines and Workshop on Project 3 </h2>
<ul>
<li> HTML:</li>
@@ -427,7 +435,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h2 id="___sec14">Week 48 November 23-27: Support Vector Machines and Summary of Course with Future Perspectives </h2>
<h2 id="week-49-november-29-december-3-support-vector-machines-and-summary-of-course-with-future-perspectives">Week 49 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives </h2>
<ul>
<li> HTML:</li>
@@ -448,7 +456,7 @@ The teaching material is produced in various formats for running codes (jupyter
<!-- !split -->
<h2 id="___sec15">Textbook </h2>
<h2 id="textbook">Textbook </h2>
<ul>
<li> PDF-file:</li>
@@ -467,9 +475,9 @@ The teaching material is produced in various formats for running codes (jupyter
<!-- !split -->
<h2 id="___sec16">Projects and Exercises Fall 2020 </h2>
<h2 id="projects-and-exercises-fall-2021-to-be-updated">Projects and Exercises Fall 2021, to be updated </h2>
<h3 id="___sec17">First homework set, week 35 </h3>
<h3 id="first-homework-set-week-35">First homework set, week 35 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -488,7 +496,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h3 id="___sec18">Second homework set, week 36 </h3>
<h3 id="second-homework-set-week-36">Second homework set, week 36 </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -507,7 +515,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h3 id="___sec19">Project 1, Deadline October 10 (available September 1) </h3>
<h3 id="project-1-deadline-october-10-available-september-1">Project 1, Deadline October 10 (available September 1) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -532,7 +540,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h3 id="___sec20">Project 2, Deadline November 13 (available October 6) </h3>
<h3 id="project-2-deadline-november-13-available-october-6">Project 2, Deadline November 13 (available October 6) </h3>
<ul>
<li> LaTeX and PDF:</li>
@@ -557,7 +565,7 @@ The teaching material is produced in various formats for running codes (jupyter
</ul>
<h3 id="___sec21">Project 3, Deadline December 16 (available November 9) </h3>
<h3 id="project-3-deadline-december-16-available-november-9">Project 3, Deadline December 16 (available November 9) </h3>
<ul>
<li> LaTeX and PDF:</li>
doc/web/tmp_mako__course.do.txt
+16 -16
View File
@@ -15,7 +15,7 @@ The teaching material is produced in various formats for running codes (jupyter
===== Week 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression =====
===== Week 35 August 23-27: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 24-28: Linear regression and review of statistics and probability theory =====
===== Week 36 August 30- September 3: Linear regression and review of statistics and probability theory =====
@@ -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 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression =====
===== Week 37 September 6-10: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge 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 7-11: Ridge and Lasso Regression =====
===== Week 38 September 13-17: Ridge and Lasso Regression =====
@@ -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 14-18: Summary of linear regression methods and start Logistic Regression =====
===== Week 39 September 20-24: 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 21-25: Logistic Regression and Gradient methods =====
===== Week 40 September 27- October 1: 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 September 28 - October 2: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model =====
===== Week 41 October 4-8: Stochastic Gradient Descent and Neural Networks, starting to build a multi-layer Perceptron model =====
@@ -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 5-9: Building a multi-layer perceptron code and introduction to Tensorflow =====
===== Week 42 October 11-15: 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 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks =====
===== Week 43 October 18-22: Deep learning (Convolutional Neural Networks and Recurrent 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 19-23: Dimesionality Reduction, Principal Component analysis =====
===== Week 44 October 25-29: Dimesionality Reduction, 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 October 26-30: Decision Trees and Bagging =====
===== Week 45 November 1-5: Decision Trees and Bagging =====
@@ -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 2-6: Random Forests and Gradient Boosting =====
===== Week 46 November 8-12: 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 9-13: Gradient boosting and Support Vector Machines =====
===== Week 47 November 15-19: 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 16-20: Support Vector Machines and Workshop on Project 3 =====
===== Week 48 November 22-26: Support Vector Machines and Workshop on Project 3 =====
@@ -183,7 +183,7 @@ 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 23-27: Support Vector Machines and Summary of Course with Future Perspectives =====
===== Week 49 November 29- December 3: Support Vector Machines and Summary of Course with Future Perspectives =====
@@ -208,7 +208,7 @@ The teaching material is produced in various formats for running codes (jupyter
!split
===== Projects and Exercises Fall 2020 =====
===== Projects and Exercises Fall 2021, to be updated =====
=== First homework set, week 35 ===
* LaTeX and PDF: