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added change to course.do.txt

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mhjensen
2020-09-16 11:20:45 +02:00
parent e2f27f942a
commit 7f8ca4a56d
4 changed files with 63 additions and 60 deletions
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doc/web/course.dlog
+3
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@@ -138,3 +138,6 @@ 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
+15 -15
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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': 'Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression',
'week35': 'Week35 August 24-28: Linear regression and review of statistics and probability theory',
'week36': 'Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression',
'week37': 'Week37 September 7-11: Ridge and Lasso Regression',
'week38': 'Week38 September 14-18: Summary of linear regression methods and start Logistic Regression',
'week39': 'Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks',
'week40': 'Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model',
'week41': 'Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)',
'week42': 'Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks',
'week43': 'Week43 October 19-23: Dimesionality Reduction, Principal Component analysis',
'week44': 'Week44 October 26-30: Decision Trees and Bagging',
'week45': 'Week45 November 2-6: Random Forests and Gradient Boosting',
'week46': 'Week46 November 9-13: Support Vector Machines',
'week47': 'Week47 November 16-20: Support Vector Machines',
'week48': 'Week48 November 23-27: Unsupervised learning, clustering and summary of course',
'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. Start Neural Networks',
'week40': 'Week 40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model',
'week41': 'Week 41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)',
'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: Support Vector Machines',
'week47': 'Week 47 November 16-20: Support Vector Machines',
'week48': 'Week 48 November 23-27: Unsupervised learning, clustering and summary of course',
}
%>
doc/web/course.html
+30 -30
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@@ -69,74 +69,74 @@ div { text-align: justify; text-justify: inter-word; }
<!-- tocinfo
{'highest level': 2,
'sections': [('Week34 August 17-21:Basic introduction to the course with '
'sections': [('Week 34 August 17-21:Basic introduction to the course with '
'schedule etc and start Linear Regression',
2,
None,
'___sec0'),
('Week35 August 24-28: Linear regression and review of '
('Week 35 August 24-28: Linear regression and review of '
'statistics and probability theory',
2,
None,
'___sec1'),
('Week36 August 31- September 4: Resampling techniques, '
('Week 36 August 31- September 4: Resampling techniques, '
'Cross-validation and Bootstrap and start discussion of Ridge '
'regression',
2,
None,
'___sec2'),
('Week37 September 7-11: Ridge and Lasso Regression',
('Week 37 September 7-11: Ridge and Lasso Regression',
2,
None,
'___sec3'),
('Week38 September 14-18: Summary of linear regression methods '
('Week 38 September 14-18: Summary of linear regression methods '
'and start Logistic Regression',
2,
None,
'___sec4'),
('Week39 September 21-25: Logistic Regression and Gradient '
('Week 39 September 21-25: Logistic Regression and Gradient '
'methods. Start Neural Networks',
2,
None,
'___sec5'),
('Week40 September 28 - October 2: Neural Networks, building a '
('Week 40 September 28 - October 2: Neural Networks, building a '
'multi-layer Perceptron model',
2,
None,
'___sec6'),
('Week41 October 5-9: Introduction to Tensorflow and deep '
('Week 41 October 5-9: Introduction to Tensorflow and deep '
'learning (Convolutional Neural Networks and Recurrent Neural '
'Networks)',
2,
None,
'___sec7'),
('Week42 October 12-16: Deep learning (Convolutional Neural '
('Week 42 October 12-16: Deep learning (Convolutional Neural '
'Networks and Recurrent Neural Networks',
2,
None,
'___sec8'),
('Week43 October 19-23: Dimesionality Reduction, Principal '
('Week 43 October 19-23: Dimesionality Reduction, Principal '
'Component analysis',
2,
None,
'___sec9'),
('Week44 October 26-30: Decision Trees and Bagging',
('Week 44 October 26-30: Decision Trees and Bagging',
2,
None,
'___sec10'),
('Week45 November 2-6: Random Forests and Gradient Boosting',
('Week 45 November 2-6: Random Forests and Gradient Boosting',
2,
None,
'___sec11'),
('Week46 November 9-13: Support Vector Machines',
('Week 46 November 9-13: Support Vector Machines',
2,
None,
'___sec12'),
('Week47 November 16-20: Support Vector Machines',
('Week 47 November 16-20: Support Vector Machines',
2,
None,
'___sec13'),
('Week48 November 23-27: Unsupervised learning, clustering and '
('Week 48 November 23-27: Unsupervised learning, clustering and '
'summary of course',
2,
None,
@@ -202,7 +202,7 @@ formulas in HTML or ipython notebook files.
</div>
<h2 id="___sec0">Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression </h2>
<h2 id="___sec0">Week 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression </h2>
<ul>
<li> HTML:</li>
@@ -221,7 +221,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec1">Week35 August 24-28: Linear regression and review of statistics and probability theory </h2>
<h2 id="___sec1">Week 35 August 24-28: Linear regression and review of statistics and probability theory </h2>
<ul>
<li> HTML:</li>
@@ -240,7 +240,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec2">Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression </h2>
<h2 id="___sec2">Week 36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression </h2>
<ul>
<li> HTML:</li>
@@ -259,7 +259,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec3">Week37 September 7-11: Ridge and Lasso Regression </h2>
<h2 id="___sec3">Week 37 September 7-11: Ridge and Lasso Regression </h2>
<ul>
<li> HTML:</li>
@@ -278,7 +278,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec4">Week38 September 14-18: Summary of linear regression methods and start Logistic Regression </h2>
<h2 id="___sec4">Week 38 September 14-18: Summary of linear regression methods and start Logistic Regression </h2>
<ul>
<li> HTML:</li>
@@ -297,7 +297,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec5">Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks </h2>
<h2 id="___sec5">Week 39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks </h2>
<ul>
<li> HTML:</li>
@@ -316,7 +316,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec6">Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model </h2>
<h2 id="___sec6">Week 40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model </h2>
<ul>
<li> HTML:</li>
@@ -335,7 +335,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec7">Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) </h2>
<h2 id="___sec7">Week 41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) </h2>
<ul>
<li> HTML:</li>
@@ -354,7 +354,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec8">Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks </h2>
<h2 id="___sec8">Week 42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks </h2>
<ul>
<li> HTML:</li>
@@ -373,7 +373,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec9">Week43 October 19-23: Dimesionality Reduction, Principal Component analysis </h2>
<h2 id="___sec9">Week 43 October 19-23: Dimesionality Reduction, Principal Component analysis </h2>
<ul>
<li> HTML:</li>
@@ -392,7 +392,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec10">Week44 October 26-30: Decision Trees and Bagging </h2>
<h2 id="___sec10">Week 44 October 26-30: Decision Trees and Bagging </h2>
<ul>
<li> HTML:</li>
@@ -411,7 +411,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec11">Week45 November 2-6: Random Forests and Gradient Boosting </h2>
<h2 id="___sec11">Week 45 November 2-6: Random Forests and Gradient Boosting </h2>
<ul>
<li> HTML:</li>
@@ -430,7 +430,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec12">Week46 November 9-13: Support Vector Machines </h2>
<h2 id="___sec12">Week 46 November 9-13: Support Vector Machines </h2>
<ul>
<li> HTML:</li>
@@ -449,7 +449,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec13">Week47 November 16-20: Support Vector Machines </h2>
<h2 id="___sec13">Week 47 November 16-20: Support Vector Machines </h2>
<ul>
<li> HTML:</li>
@@ -468,7 +468,7 @@ formulas in HTML or ipython notebook files.
</ul>
<h2 id="___sec14">Week48 November 23-27: Unsupervised learning, clustering and summary of course </h2>
<h2 id="___sec14">Week 48 November 23-27: Unsupervised learning, clustering and summary of course </h2>
<ul>
<li> HTML:</li>
doc/web/tmp_mako__course.do.txt
+15 -15
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@@ -29,7 +29,7 @@ formulas in HTML or ipython notebook files.
===== Week34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression =====
===== Week 34 August 17-21:Basic introduction to the course with schedule etc and start Linear Regression =====
@@ -41,7 +41,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week34/ipynb/week34.ipynb"
===== Week35 August 24-28: Linear regression and review of statistics and probability theory =====
===== Week 35 August 24-28: Linear regression and review of statistics and probability theory =====
@@ -53,7 +53,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week35/ipynb/week35.ipynb"
===== Week36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression =====
===== Week 36 August 31- September 4: Resampling techniques, Cross-validation and Bootstrap and start discussion of Ridge regression =====
@@ -65,7 +65,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week36/ipynb/week36.ipynb"
===== Week37 September 7-11: Ridge and Lasso Regression =====
===== Week 37 September 7-11: Ridge and Lasso Regression =====
@@ -77,7 +77,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week37/ipynb/week37.ipynb"
===== Week38 September 14-18: Summary of linear regression methods and start Logistic Regression =====
===== Week 38 September 14-18: Summary of linear regression methods and start Logistic Regression =====
@@ -89,7 +89,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week38/ipynb/week38.ipynb"
===== Week39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks =====
===== Week 39 September 21-25: Logistic Regression and Gradient methods. Start Neural Networks =====
@@ -101,7 +101,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week39/ipynb/week39.ipynb"
===== Week40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model =====
===== Week 40 September 28 - October 2: Neural Networks, building a multi-layer Perceptron model =====
@@ -113,7 +113,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week40/ipynb/week40.ipynb"
===== Week41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) =====
===== Week 41 October 5-9: Introduction to Tensorflow and deep learning (Convolutional Neural Networks and Recurrent Neural Networks) =====
@@ -125,7 +125,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week41/ipynb/week41.ipynb"
===== Week42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks =====
===== Week 42 October 12-16: Deep learning (Convolutional Neural Networks and Recurrent Neural Networks =====
@@ -137,7 +137,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week42/ipynb/week42.ipynb"
===== Week43 October 19-23: Dimesionality Reduction, Principal Component analysis =====
===== Week 43 October 19-23: Dimesionality Reduction, Principal Component analysis =====
@@ -149,7 +149,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week43/ipynb/week43.ipynb"
===== Week44 October 26-30: Decision Trees and Bagging =====
===== Week 44 October 26-30: Decision Trees and Bagging =====
@@ -161,7 +161,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week44/ipynb/week44.ipynb"
===== Week45 November 2-6: Random Forests and Gradient Boosting =====
===== Week 45 November 2-6: Random Forests and Gradient Boosting =====
@@ -173,7 +173,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week45/ipynb/week45.ipynb"
===== Week46 November 9-13: Support Vector Machines =====
===== Week 46 November 9-13: Support Vector Machines =====
@@ -185,7 +185,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week46/ipynb/week46.ipynb"
===== Week47 November 16-20: Support Vector Machines =====
===== Week 47 November 16-20: Support Vector Machines =====
@@ -197,7 +197,7 @@ formulas in HTML or ipython notebook files.
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/week47/ipynb/week47.ipynb"
===== Week48 November 23-27: Unsupervised learning, clustering and summary of course =====
===== Week 48 November 23-27: Unsupervised learning, clustering and summary of course =====