diff --git a/doc/web/course.dlog b/doc/web/course.dlog
index 92aadb3cb..fe157228c 100644
--- a/doc/web/course.dlog
+++ b/doc/web/course.dlog
@@ -249,3 +249,6 @@ 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 f3f96caf3..2895b92b0 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 21-25:Basic introduction to the course with schedule etc and start Linear Regression',
- 'week35': 'Week 35 August 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics',
- 'week36': 'Week 36 September 4-8: Statistical analysis and discussion of Ridge and Lasso regression',
- 'week37': 'Week 37 September 11-15: Resampling techniques, Cross-validation and the Bootstrap',
- 'week38': 'Week 38 September 18-22: Summary of linear regression methods and start Logistic Regression',
- 'week39': 'Week 39 September 25-29: Logistic Regression and Gradient methods',
+ 'week34': 'Week 34 August 19-23:Basic introduction to the course with schedule etc and start Linear Regression',
+ 'week35': 'Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics',
+ 'week36': 'Week 36 September 2-6: Statistical analysis and discussion of Ridge and Lasso regression',
+ 'week37': 'Week 37 September 9-13: Resampling techniques, Cross-validation and the Bootstrap',
+ 'week38': 'Week 38 September 16-20: Summary of linear regression methods and start Logistic Regression',
+ 'week39': 'Week 39 September 23-27: Logistic Regression and Gradient methods',
'week40': 'Week 40 October 2-6: Stochastic Gradient Descent',
- 'week41': 'Week 41 October 9-13: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm',
- 'week42': 'Week 42 October 16-20: Building a multi-layer perceptron code and introduction to Tensorflow',
- 'week43': 'Week 43 October 23-27: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)',
- 'week44': 'Week 44 October 30- November 3: Convolutional Neural Networks',
- 'week45': 'Week 45 November 6-10: Convolutional neural networks and Recurrent neural networks',
- 'week46': 'Week 46 November 13-17: Decision trees, random forests and other ensemble methods',
- 'week47': 'Week 47 November 20-24: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives',
+ 'week41': 'Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm',
+ 'week42': 'Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow',
+ 'week43': 'Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)',
+ 'week44': 'Week 44 October 28- November 1: Convolutional Neural Networks',
+ 'week45': 'Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks',
+ 'week46': 'Week 46 November 11-15: Decision trees, random forests and other ensemble methods',
+ 'week47': 'Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives',
}
%>
diff --git a/doc/web/course.html b/doc/web/course.html
index e8dc6040c..79e9f70da 100644
--- a/doc/web/course.html
+++ b/doc/web/course.html
@@ -117,75 +117,74 @@ div.toc p,a {
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 21-25:Basic introduction to the course with schedule etc and start Linear Regression
+Week 34 August 19-23:Basic introduction to the course with schedule etc and start Linear Regression
- HTML:
@@ -239,7 +238,7 @@ end of tocinfo -->
- ipynb file
-Week 35 August 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics
+Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics
- HTML:
@@ -253,7 +252,7 @@ end of tocinfo -->
- ipynb file
-Week 36 September 4-8: Statistical analysis and discussion of Ridge and Lasso regression
+Week 36 September 2-6: Statistical analysis and discussion of Ridge and Lasso regression
- HTML:
@@ -267,7 +266,7 @@ end of tocinfo -->
- ipynb file
-Week 37 September 11-15: Resampling techniques, Cross-validation and the Bootstrap
+Week 37 September 9-13: Resampling techniques, Cross-validation and the Bootstrap
- HTML:
@@ -281,7 +280,7 @@ end of tocinfo -->
- ipynb file
-Week 38 September 18-22: Summary of linear regression methods and start Logistic Regression
+Week 38 September 16-20: Summary of linear regression methods and start Logistic Regression
- HTML:
@@ -295,7 +294,7 @@ end of tocinfo -->
- ipynb file
-Week 39 September 25-29: Logistic Regression and Gradient methods
+Week 39 September 23-27: Logistic Regression and Gradient methods
- HTML:
@@ -323,7 +322,7 @@ end of tocinfo -->
- ipynb file
-Week 41 October 9-13: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm
+Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm
- HTML:
@@ -337,7 +336,7 @@ end of tocinfo -->
- ipynb file
-Week 42 October 16-20: Building a multi-layer perceptron code and introduction to Tensorflow
+Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow
- HTML:
@@ -351,7 +350,7 @@ end of tocinfo -->
- ipynb file
-Week 43 October 23-27: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)
+Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks)
- HTML:
@@ -365,7 +364,7 @@ end of tocinfo -->
- ipynb file
-Week 44 October 30- November 3: Convolutional Neural Networks
+Week 44 October 28- November 1: Convolutional Neural Networks
- HTML:
@@ -379,7 +378,7 @@ end of tocinfo -->
- ipynb file
-Week 45 November 6-10: Convolutional neural networks and Recurrent neural networks
+Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks
- HTML:
@@ -393,7 +392,7 @@ end of tocinfo -->
- ipynb file
-Week 46 November 13-17: Decision trees, random forests and other ensemble methods
+Week 46 November 11-15: Decision trees, random forests and other ensemble methods
- HTML:
@@ -407,7 +406,7 @@ end of tocinfo -->
- ipynb file
-Week 47 November 20-24: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives
+Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives
- HTML:
diff --git a/doc/web/tmp_mako__course.do.txt b/doc/web/tmp_mako__course.do.txt
index c75e46d82..23a10b96b 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 21-25:Basic introduction to the course with schedule etc and start Linear Regression =====
+===== Week 34 August 19-23: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 28- September 1: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics =====
+===== Week 35 August 26-30: Linear regression, from ordinary Least Squares to Ridge and Lasso Regression, Elements of Statistics =====
@@ -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 4-8: Statistical analysis and discussion of Ridge and Lasso regression =====
+===== Week 36 September 2-6: 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 11-15: Resampling techniques, Cross-validation and the Bootstrap =====
+===== Week 37 September 9-13: 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 18-22: Summary of linear regression methods and start Logistic Regression =====
+===== Week 38 September 16-20: 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 25-29: Logistic Regression and Gradient methods =====
+===== Week 39 September 23-27: Logistic Regression and Gradient methods =====
@@ -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 9-13: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm =====
+===== Week 41 October 7-11: Neural Networks, starting to build a multi-layer Perceptron model, the Back Propagation algoritm =====
@@ -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 16-20: Building a multi-layer perceptron code and introduction to Tensorflow =====
+===== Week 42 October 14-18: Building a multi-layer perceptron code and introduction to Tensorflow =====
@@ -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 23-27: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks) =====
+===== Week 43 October 21-25: Deep learning, Solving Differential Equations with NNs and Convolutional Neural Networks) =====
@@ -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 30- November 3: Convolutional Neural Networks =====
+===== Week 44 October 28- November 1: Convolutional Neural Networks =====
@@ -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 6-10: Convolutional neural networks and Recurrent neural networks =====
+===== Week 45 November 4-8: Convolutional neural networks and Recurrent neural networks =====
@@ -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 13-17: Decision trees, random forests and other ensemble methods =====
+===== Week 46 November 11-15: Decision trees, random forests and other ensemble methods =====
@@ -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 20-24: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives =====
+===== Week 47 November 18-22: Ensemble methods, Bagging and Boosting and Summary of Course with Future Perspectives =====