Preface Acknowledgments About the Author Notation CHAPTER 1 Introduction 1.1 The Historical Context 1.2 Artificia Intelligenceand Machine Learning 1.3 Algorithms Can Learn WhatIs Hidden in the Data 1.4 Typical Applications of Machine Learning Speech Recognition Computer Vision Multimodal Data Natural Language Processing Robotics Autonomous Cars Challenges for the Future 1.5 Machine Learning: Major Directions 1.5.1 Supervised Learning 1.6 Unsupervised and Semisupervised Learning 1.7 Structure and a Road Map of the Book References CHAPTER 2 Probability and Stochastic Processes 2.1 Introduction 2.2 Probability and Random Variables 2.2.1 Probability 2.2.2 Discrete Random Variables 2.2.3 Continuous Random Variables 2.2.4 Meanand Variance 2.2.5 Transformation of Random Variables 2.3 Examples of Distributions 2.3.1 Discrete Variables 2.3.2 Continuous Variables 2.4 Stochastic Processes 2.4.1 First-and Second-Order Statistics 2.4.2 Stationarity and Ergodicity 2.4.3 Power Spectral Density 2.4.4 Autoregressive Models 2.5 Information Theory 2.5.1 Discrete Random Variables 2.5.2 Continuous Random Variables 2.6 Stochastic Convergence Convergence Everywhere Convergence Almost Everywhere Convergence in the Mean-Square Sense Convergence in Probability Convergence in Distribution Problems References CHAPTER 3 Learning in Parametric Modeling: Basic Concepts and Directions 3.1 Introduction
3.2 Parameter Estimation: the Deterministic Point of View 3.3 Linear Regression 3.4 Classifcation Generative Versus Discriminative Learning 3.5 Biased Versus Unbiased Estimation 3.5.1 Biased or Unbiased Estimation 3.6 The Cramer-Rao Lower Bound 3.7 Suffcient Statistic 3.8 Regularization Inverse Problems: Ill-Conditioning and Overfittin 3.9 The Bias-Variance Dilemma 3.9.1 Mean-Square Error Estimation 3.9.2 Bias-Variance Tradeoff 3.10 Maximum Likelihood Method 3.10.1 Linear Regression: the Nonwhite Gaussian Noise Case 3.11 Bayesian Inference 3.11.1 The Maximum a Posteriori Probability Estimation Method 3.12 Curse of Dimensionality 3.13 Validation Cross-Validation 3.14 Expected Loss and Empirical Risk Functions Learnability 3.15 Nonparametric Modeling and Estimation Problems MATLAB? Exercises References CHAPTER 4 Mean-Square Error Linear Estimation 4.1 Introduction 4.2 Mean-Square Error Linear Estimation: the Normal Equations 4.2.1 The Cost Function Surface 4.3 A Geometric Viewpoint: Orthogonality Condition …… CHAPTER 5 Online Learning: the Stochastic Gradient Descent Family of Algorithms CHAPTER 6 The Least-Squares Family CHAPTER 7 Classification: a Tour of the Classics CHAPTER 8 Parameter Learning: a Convex Analytic Path CHAPTER 9 Sparsity-Aware Learning: Concepts and Theoretical Foundations CHAPTER 10 Sparsity-Aware Learning: Algorithms and Applications CHAPTER 11 Learning in Reproducing Kernel Hilbert Spaces CHAPTER 12 Bayesian Learning: Inference and the EM Algorithm CHAPTER 13 Bayesian Learning: Approximate Inference and Nonparametric Models CHAPTER 14 Monte Carlo Methods CHAPTER 15 Probabilistic Graphical Models: Part Ⅰ CHAPTER 16 Probabilistic Graphical Models: Part Ⅱ CHAPTER 17 Particle Filtering CHAPTER 18 Neural Networks and Deep Learning CHAPTER 19 Dimensionality Reduction and Latent Variable Modeling Index
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