ML 必读书籍

除了以下推荐的书以外，出版在Foundations and Trends in Machine Learning上面的survey文章都值得一看。 
  
  
入门： 
  
统计学习方法

李航

Pattern Recognition And Machine Learning 
  
Christopher M. Bishop 
  
Machine Learning : A Probabilistic Perspective 
  
Kevin P. Murphy 
  
The Elements of Statistical Learning : Data Mining, Inference, and Predictio 
n 
  
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, Optimizatio 
n, 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 Le 
arning) 
  
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 I 
mplementation 
  
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