"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/