《神经网络与机器学习（英文版第3版）》特色：
　　基于随机梯度下降的在线学习算法；小规模和大规模学习问题。
　　核方法，包括支持向量机和表达定理。
　　信息论学习模型，包括连接、独立分量分析（ICA），一致独立分量分析和信息瓶颈。
　　随机动态规划，包括逼近和神经动态规划。
　　逐次状态估计算法，包括Kalman和粒子滤波器。
　　利用逐次状态估计算法训练递归神经网络。
　　富有洞察力的面向计算机的试验。

　　In writing this third edition of a classic book， I have been guided by the same uuderly hag philosophy of the first edition of the book:
　　Write an up wdate treatment of neural networks in a comprehensive, thorough, and read able manner.The new edition has been retitied Neural Networks and Learning Machines, in order toreflect two reahties: L The perceptron, the multilayer perceptroo, self organizing maps, and neuro
　　dynamics, to name a few topics, have always been considered integral parts of neural networks, rooted in ideas inspired by the human brain.2. Kernel methods, exemplified by support vector machines and kernel principal components analysis, are rooted in statistical learning theory.Although, indeed, they share many fundamental concepts and applications, there aresome subtle differences between the operations of neural networks and learning ma chines. The underlying subject matter is therefore much richer when they are studiedtogether, under one umbrella, particulasiy so when ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either one operating on its own, and ideas inspired by the human brain lead to new perspectives wherever they are of particular importance.

Preface
Acknowledgements
Abbreviations and Symbols
GLOSSARY
Introduction
1 Whatis aNeuralNetwork?
2 The Human Brain
3 Models of a Neuron
4 Neural Networks Viewed As Dirccted Graphs
5 Feedback
6 Network Architecturns
7　Knowledge Representation
8 Learning Processes
9 Learninglbks
10 Concluding Remarks
Notes and Rcferences

Chapter 1 Rosenblatts Perceptrou
1.1 Introduction
1.2 Perceptron
1.3 1he Pcrceptron Convergence Theorem
1.4 Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment
1.5 Computer Experiment：Pattern Classification
1.6 The Batch Perceptron Algorithm
1.7 Summary and Discussion
Notes and Refercnces
Problems

Chapter 2 Model Building through Regression
2.1 Introduction 68
2.2 Linear Regression Model：Preliminary Considerafions
2.3 Maximum a Posteriori Estimation ofthe ParameterVector
2.4 Relationship Between Regularized Least-Squares Estimation and MAP Estimation
2.5 Computer Experiment：Pattern Classification
2.6 The Minimum.Description-Length Principle
2.7 Rnite Sample—Size Considerations
2.8 The Instrumental，variables Method
2 9 Summary and Discussion
Notes and References
Problems

Chapter 3 The Least—Mean-Square Algorithm
3.1 Introduction
3.2 Filtering Structure of the LMS Algorithm
3.3 Unconstrained optimization：a Review
3.4 ThC Wiener FiIter
3.5 ne Least.Mean.Square Algorithm
3.6 Markov Model Portraying the Deviation of the LMS Algorithm from the Wiener Filter
3.7 The Langevin Equation：Characterization ofBrownian Motion
3.8 Kushner’S Direct.Averaging Method
3.9 Statistical LMS Learning Iheory for Sinail Learning—Rate Parameter
3.10 Computer Experiment I:Linear PTediction
3.11 Computer Experiment II：Pattern Classification
3.12 Virtucs and Limitations of the LMS AIgorithm
3.13 Learning.Rate Annealing Schedules
3.14 Summary and Discussion
Notes and Refefences
Problems

Chapter 4 Multilayer Pereeptrons
4.1 IntroductlOn
4.2 Some Preliminaries
4.3 Batch Learning and on.Line Learning
4.4 The Back.Propagation Algorithm
4 5 XORProblem
4.6 Heuristics for Making the Back—Propagation Algorithm PerfoITn Better
4.7 Computer Experiment：Pattern Classification
4.8 Back Propagation and Differentiation
4.9 The Hessian and lIs Role 1n On-Line Learning
4.10 Optimal Annealing and Adaptive Control of the Learning Rate
4.11 Generalization
4.12 Approximations of Functions
4.13 Cross.Vjlidation
4.14 Complexity Regularization and Network Pruning
4.15 Virtues and Limitations of Back-Propagation Learning
4.16 Supervised Learning Viewed as an Optimization Problem
4.17 COUVOlutionaI Networks
4.18 Nonlinear Filtering
4.19 Small—Seale VerSus Large+Scale Learning Problems
4.20 Summary and Discussion
Notes and RCfcreilces
Problems

Chapter 5 Kernel Methods and Radial-Basis Function Networks
5.1 Intreduction
5.2 Cover’S Theorem on the Separability of Patterns
5.3 1he Interpolation Problem
5 4 Radial—Basis—Function Networks
5.5 K.Mcans Clustering
5.6 Recursive Least-Squares Estimation of the Weight Vector
5 7 Hybrid Learning Procedure for RBF Networks
5 8 Computer Experiment：Pattern Classification
5.9 Interpretations of the Gaussian Hidden Units
5.10 Kernel Regression and Its Relation to RBF Networks
5.11 Summary and Discussion
Notes and References
Problems
Chapter 6 Support Vector Machines
Chapter 7 Regularization Theory
Chapter 8 Prindpal-Components Aaalysis
Chapter 9 Self-Organizing Maps
Chapter 10 Information-Theoretic Learning Models
Chapter 11 Stochastic Methods Rooted in Statistical Mechanics
Chapter 12 Dynamic Programming
Chapter 13 Neurodynamics
Chapter 14 Bayseian Filtering for State Estimation ofDynamic Systems
Chaptel 15 Dynamlcaay Driven Recarrent Networks
Bibliography
Index