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