Hardcover: 445 pages Publisher: The MIT Press (October 1, 2004) Language: English  
Introduction to Machine Learning 机器学习导论 by Ethem Alpaydin
这本书有中文的翻译版 < 机器学习导论> ，译者：范明；昝红英；牛常勇


现在给大家英文的原版

Contents
Series Foreword xiii
Figures xv
Tables xxiii
Preface xxv
Acknowledgments xxvii
Notations xxix
1 Introduction 1
1.1 What Is Machine Learning? 1
1.2 Examples of Machine Learning Applications 3
1.2.1 Learning Associations 3
1.2.2 Classification 4
1.2.3 Regression 8
1.2.4 Unsupervised Learning 10
1.2.5 Reinforcement Learning 11
1.3 Notes 12
1.4 Relevant Resources 14
1.5 Exercises 15
1.6 References 16
2 Supervised Learning 17
2.1 Learning a Class from Examples 17
2.2 Vapnik-Chervonenkis (VC) Dimension 22
2.3 Probably Approximately Correct (PAC) Learning 24
2.4 Noise 25
2.5 Learning Multiple Classes 27
2.6 Regression 29
2.7 Model Selection and Generalization 32
2.8 Dimensions of a Supervised Machine Learning Algorithm 35
2.9 Notes 36
2.10 Exercises 37
2.11 References 38
3 Bayesian Decision Theory 39
3.1 Introduction 39
3.2 Classification 41
3.3 Losses and Risks 43
3.4 Discriminant Functions 45
3.5 Utility Theory 46
3.6 Value of Information 47
3.7 Bayesian Networks 48
3.8 Influence Diagrams 55
3.9 Association Rules 56
3.10 Notes 57
3.11 Exercises 57
3.12 References 58
4 Parametric Methods 61
4.1 Introduction 61
4.2 Maximum Likelihood Estimation 62
4.2.1 Bernoulli Density 62
4.2.2 Multinomial Density 63
4.2.3 Gaussian (Normal) Density 64
4.3 Evaluating an Estimator: Bias and Variance 64
4.4 The Bayes' Estimator 67
4.5 Parametric Classification 69
4.6 Regression 73
4.7 Tuning Model Complexity: BiasjVariance Dilemma 76
4.8 Model Selection Procedures 79
4.9 Notes 82
4.10 Exercises 82
4.11 References 83
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