另外很多优秀书籍不详细介绍：

入门：
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 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, 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
除了以上推荐的书以外，出版在Foundations and Trends in Machine Learning上面的survey文章也都值得一看。