ESL

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

References

IntroToDeepLearning