转：http://isilic.iteye.com/blog/1851048
决策树的重要性和入门可以参考前面两篇文章：
在清华水木上有个Machine Learning的书单: http://www.newsmth.net/nForum/#!article/AI/34859
其中作为入门的几本书也不简单，都是经典的作品PRML或者是最新的著作(ML-APP)，这些书在网上都能找到，不过找到不过不看放在硬盘里的话，其实这些书对你的用处并不大。
这些书都能在网上找到，我就不贴下载了，大家可以自行查找。
入门：
Pattern Recognition And Machine Learning
Author：hristopher M. Bishop
Machine Learning : A Probabilistic Perspective
Kevin P. Murphy
The Elements of Statistical Learning : Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Information Theory, Inference and Learning Algorithms
David J. C. MacKay
All of Statistics : A Concise Course in Statistical Inference
Larry Wasserman
优化：
Convex Optimization
Stephen Boyd, Lieven Vandenberghe
Numerical Optimization
Jorge Nocedal, Stephen Wright
Optimization for Machine Learning
Suvrit Sra, Sebastian Nowozin, Stephen J. Wright
核方法：
Kernel Methods for Pattern Analysis
John Shawe-Taylor, Nello Cristianini
Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schlkopf, Alexander J. Smola
半监督：
Semi-Supervised Learning
Olivier Chapelle
高斯过程：
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Carl Edward Rasmussen, Christopher K. I. Williams
概率图模型：
Graphical Models, Exponential Families, and Variational Inference
Martin J Wainwright, Michael I Jordan
Boosting:
Boosting : Foundations and Algorithms
Schapire, Robert E.; Freund, Yoav
贝叶斯:
Statistical Decision Theory and Bayesian Analysis
James O. Berger
The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation
Christian P. Robert
Bayesian Nonparametrics
Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker
Principles of Uncertainty
Joseph B. Kadane
Decision Theory : Principles and Approaches
Giovanni Parmigiani, Lurdes Inoue
蒙特卡洛：
Monte Carlo Strategies in Scientific Computing
Jun S. Liu
Monte Carlo Statistical Methods
Christian P.Robert, George Casella
信息几何：
Methods of Information Geometry
Shun-Ichi Amari, Hiroshi Nagaoka
Algebraic Geometry and Statistical Learning Theory
Watanabe, Sumio
Differential Geometry and Statistics
M.K. Murray, J.W. Rice
渐进收敛：
Asymptotic Statistics
A. W. van der Vaart
Empirical Processes in M-estimation
Geer, Sara A. van de
不推荐：
Statistical Learning Theory
Vladimir N. Vapnik
Bayesian Data Analysis, Second Edition
Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin
Probabilistic Graphical Models : Principles and Techniques
Daphne Koller, Nir Friedman
另外在微博上也有北美比较常用的机器学习/自然语言处理/语音处理经典书籍的推荐，其中的推荐面比较广，可以看下，和水木上的推荐有重叠。