diff --git a/README.md b/README.md
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+++ b/README.md
@@ -231,91 +231,91 @@ For the reading assignments we use the following abbreviations:
 
 Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics) of Goodfellow et al. and Bishop chapter 1 and chapter 2. These chapters give a relevant background to the basic mathematical and statistical foundations of the course. Parts of these chapters will be covered during the lectures the first three weeks.
 
-###  Week 35 August 23-27
+###  Week 34 August 23-27
 - Lab Wednesday: Introduction to software and repetition of Python Programming
 - Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression
 - Lecture Friday: Basics of Linear Regression
 - Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
 
-###  Week 36 August 30-September 3
+###  Week 35 August 30-September 3
 - Lab Wednesday: 
 - Lecture Thursday: Linear Regression, from ordinary linear regression to Ridge and Lasso regression, linear algebra analysis, examples and discussions of codes
 - Lecture Friday: Linear Regression, Linear algebra and Ridge and Lasso Regression, linear algebra analysis, examples and discussions of codes
 - Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
 
-###  Week 37 September 6-10
+###  Week 36 September 6-10
 - Lab Wednesday: 
 - Lecture Thursday: Statistical interpretation of Linear Regression
 - Lecture Friday: Bias-Variance tradeoff
 - Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
   - Chapter
-###  Week 38 September 13-17
+###  Week 37 September 13-17
 - Lab Wednesday: 
 - Lecture Thursday: Resampling methods, cross-validation and Bootstrap
 - Lecture Friday: More on Resampling methods and summary of linear regression
 - Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Chapter
-###  Week 39 September 20-24
+###  Week 38 September 20-24
 - Lab Wednesday: 
 - Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
 - Lecture Friday: Logistic Regression and gradient optimization
 - Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Chapter
-###  Week 40 September 27- October 1
+###  Week 39 September 27- October 1
 - Lab Wednesday: 
 - Lecture Thursday: Gradient Optimization methods
 - Lecture Friday:  Deep Learning and Neural Networks
 - Reading recommendations: See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Chapter
-###  Week 41 October 4-8
+###  Week 40 October 4-8
 - Lab Wednesday: 
 - Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
 - Lecture Friday:  Deep Learning and TensorFlow and Keras
 - Reading recommendations:
   - Chapter
-###  Week 42 October 11-15
+###  Week 41 October 11-15
 - Lab Wednesday: 
 - Lecture Thursday: Deep learning and Neural Networks
 - Lecture Friday: Convolutional Neural Networks, basic elements
 - Reading recommendations:
   - GoodFellow et al, Chapter 9
-###  Week 43 October 18-22
+###  Week 42 October 18-22
 - Lab Wednesday: 
 - Lecture Thursday: Convolutional Neural Networks and classification problems
 - Lecture Friday: Convolutional Neural Networks and classification problems
 - Reading recommendations:
   - Chapter
-###  Week 44 October 25-29
+###  Week 43 October 25-29
 - Lab Wednesday: 
 - Lecture Thursday:  Recurrent Neural Networks
 - Lecture Friday: Recurrent Neural Networks and time series
 - Reading recommendations:
   - Chapter
-###  Week 45 November 1-5
+###  Week 44 November 1-5
 - Lab Wednesday: 
 - Lecture Thursday: Decision trees, classification and regression
 - Lecture Friday: Decision trees, basic algorithms
 - Reading recommendations:
   - Chapter
-###  Week 46 November 8-12 
+###  Week 45 November 8-12 
 - Lab Wednesday: 
 - Lecture Thursday: Ensemble methods, bagging and random forests
 - Lecture Friday: Boosting and gradient boosting
 - Reading recommendations:
   - Chapter
-###  Week 47 November 15-19
+###  Week 46 November 15-19
 - Lab Wednesday: 
 - Lecture Thursday: 
 - Lecture Friday: Unsupervised Learning, k-means
 - Reading recommendations:
   - Chapter
-###  Week 48 November 22-26
+###  Week 47 November 22-26
 - Lab Wednesday: 
 - Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)
 - Lecture Friday:  Unsupervised Learning and PCA and Clustering
 - Reading recommendations:
   - Chapter
-###  Week 49 November 29- December 2
+###  Week 48 November 29- December 2
 - Lab Wednesday: 
 - Lecture Thursday: Unsupervised Learning and Clustering
 - Lecture Friday:  Summary of course