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@@ -235,37 +235,37 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)
- 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
- 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 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
### 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
- Reading recommendations: See lecture notes for week 35 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 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
- Lecture Thursday: Ridge and Lasso regression and the SVD. Statistical interpretation of Linear Regression
- Lecture Friday: Further interpretations of Linear regression.
- Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
- Chapter
### 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.
- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### 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.
- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### 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.
- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### Week 40 October 4-8
- Lab Wednesday: