经典原版书库机器学习:贝叶斯和优化方法(英文版 原书第2版)

Prefaceiv
Acknowledgmentsvi
About the Authorviii
Notationix
CHAPTER1 Introduction1
11 The Historical Context1
12 Artificia Intelligenceand Machine Learning2
13 Algorithms Can Learn WhatIs Hidden in the Data4
14 Typical Applications of Machine Learning6
Speech Recognition6
Computer Vision6
Multimodal Data6
Natural Language Processing7
Robotics7
Autonomous Cars7
Challenges for the Future8
15 Machine Learning: Major Directions8
151 Supervised Learning8
16 Unsupervised and Semisupervised Learning11
17 Structure and a Road Map of the Book12
References16
CHAPTER2 Probability and Stochastic Processes19
21 Introduction20
22 Probability and Random Variables20
221 Probability20
222 Discrete Random Variables22
223 Continuous Random Variables24
224 Meanand Variance25
225 Transformation of Random Variables28
23 Examples of Distributions29
231 Discrete Variables29
232 Continuous Variables32
24 Stochastic Processes41
241 First-and Second-Order Statistics42
242 Stationarity and Ergodicity43
243 Power Spectral Density46
244 Autoregressive Models51
25 Information Theory54
251 Discrete Random Variables56
252 Continuous Random Variables59
26 Stochastic Convergence61
Convergence Everywhere62
Convergence Almost Everywhere62
Convergence in the Mean-Square Sense62
Convergence in Probability63
Convergence in Distribution63
Problems63
References65
CHAPTER3 Learning in Parametric Modeling: Basic Concepts and Directions67
31 Introduction67
32 Parameter Estimation: the Deterministic Point of View68
33 Linear Regression71
34Classifcation75
Generative Versus Discriminative Learning78
35 Biased Versus Unbiased Estimation80
351 Biased or Unbiased Estimation?81
36 The Cram閞朢ao Lower Bound83
37 Suffcient Statistic87
38 Regularization89
Inverse Probl