"Elements of Statistical Learning: Data mining, Inference, and Prediction" 第二版统计学习: 数据挖掘, 分析和预测

斯坦福大学统计系的教材 可以说是机器学习教材的典范

研究算法交易 高频交易的话 看看这个还是不错的 内容深入浅出
有简单的线性回归 也有 复杂的


这是目录:

Chapter What's new
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression LAR algorithm and generalizations of the lasso
4. Linear Methods for Classification Lasso path for logistic regression
5. Basis Expansions and Regularization Additional illustrations of RKHS
6. Kernel Smoothing Methods
7. Model Assessment and Selection Strengths and pitfalls of cross-validation
8. Model Inference and Averaging
9. Additive Models, Trees, and
Related Methods
10. Boosting and Additive Trees New example from ecology; some material split off to Chapter 16.
11. Neural Networks Bayesian neural nets and the NIPS 2003 challenge
12. Support Vector Machines and Flexible Discriminants Path algorithm for SVM classifier
13. Prototype Methods and Nearest-Neighbors
14. Unsupervised Learning Spectral clustering, kernel PCA, sparse PCA, non-negative matrix factorization archetypal analysis, nonlinear dimension reduction, Google page rank algorithm, a direct approach to ICA
15. Random Forests New
16. Ensemble Learning New
17. Undirected Graphical Models New
18. High-Dimensional Problems New
附豆瓣的书评:
http://book.douban.com/subject/3294335/