Pattern Recognition and Machine Learning
by Christopher M. Bishop
Publisher:  Springer  2006
Number Of Pages:   750
Contents：
1 Introduction
2 Probability Distributions
3 Linear Models for Regression
4 Linear Models for Classification
5 Neural Networks
6 Kernel Methods
7 Sparse Kernel Machines
8 Graphical Models
9 Mixture Models and EM
10 Approximate Inference
11 Sampling Methods
12 Continuous Latent Variables
13 Sequential Data
14 Combining Models

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