All the material?that we have covered in class (except for conformal prediction)?make up the syllabus for the exam, including the weekly exercises. In terms of the course book, we have covered the following:
- Chapter 1 - Introduction
- The whole chapter
- Chapter 2 -?Overview of Supervised Learning
- The whole chapter
- Chapter 3 -?Linear Methods for Regression
- Sections 3.1-3.6, 3.8
- Chapter 4 -?Linear Methods for Classification
- Sections 4.2, 4.4.1-4.4.4
- Chapter 5 - Basis Expansions and Regularization
- Sections 5.1-5.2, 5.4-5.5, 5.7
- Chapter 6 - Kernel Smoothing Methods
- Sections 6.1-6.6, 6.8
- Chapter 7 - Model Assessment and Selection
- Sections 7.1-7.7, 7.10-7.11
- Chapter 8 - Model Inference and Averaging
- Section 8.7
- Chapter 9 - Additive Models, Trees, and Related Methods
- Sections 9.1-9.2
- Chapter 10 - Boosting and Additive Trees
- Sections 10.1-10.6, 10.9-10.12.1
- Chapter 11 - Neural Networks
- Sections 11.1-11.8
- Chapter 13 - Prototype Methods and Nearest-Neighbors
- Section 13.4
- Chapter 15 - Random Forests
- The whole chapter