The Elements of Statistical Learning with PyTorch
- Chapter 2 - Overview of Supervised Learning
- Chapter 3 - Linear Methods for Regression
- Chapter 4 - Linear Methods for Classification
- Chapter 5 - Basis Expansions and Regularization
- Chapter 6 - Kernel Smoothing Methods
- Chapter 7 - Model Assessment and Selection
- Chapter 8 - Model Inference and Averaging
- Chapter 9 - Additive Models, Trees, and Related Methods
- Chapter 10 - Boosting and Additive Trees
- Chapter 11 - Neural Networks
- Chapter 12 - Support Vector Machines and Flexible Discriminants
- Chapter 13 - Prototype Methods and Nearest-Neighbors
- Chapter 14 - Unsupervised Learning
- Chapter 15 - Random Forests