| Contents Notes |
Introduction -- Overview of supervised learning -- Linear methods of regression -- Linear methods for classification -- Basis expansions and regularization -- Kernel methods -- Model assessment and selection -- Model interference and averaging -- Additive methods, trees and related methods -- Boosting and additive trees -- Neural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest-neighbors -- Unsupervised learning. Describes important statistical ideas in machine learning, data mining, and bioinformatics. Covers a broad range, from supervised learning (prediction), to unsupervised learning, including classification trees, neural networks, and support vector machines. |
