给大家推荐机器学习的一些书籍（转）

寒泣露

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。
  
  
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