机器学习数目推荐

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。