- Introduction
- Overview of Supervised Learning
- Linear Methods for Regression
- Linear Methods for Classification
- Basic Expansions and Regularization
- Kernel Smoothing Methods
- Model Assessment and Selection
- Model Inference and Averaging
- Additive Models, Trees, and Related Methods
- Boosting and Additive Trees
- Neural Networks
- Support Vector Machines and Flexible Discriminants
- Prototype Methods and Nearest-Neighbors
- Unsupervised Learning
- Random Forests
- Ensemble Learning
- Undirected Graphical Models
- High-Dimensional Problems: \(p » N\)
The Elements of Statistical Learning: Data Mining, Inference and Prediction.
Trevor Hastie; Robert Tibshirani; Jerome Friedman.
web.stanford.edu .
Jan 13, 2017.