Machine Learning

Table of Contents

Cover image

Title page

Copyright

Preface

Acknowledgments

Notation

Dedication

Chapter 1: Introduction

Abstract

1.1 What Machine Learning is About

1.2 Structure and a Road Map of the Book

Chapter 2: Probability and Stochastic Processes

Abstract

2.1 Introduction

2.2 Probability and Random Variables

2.3 Examples of Distributions

2.4 Stochastic Processes

2.5 Information Theory

2.6 Stochastic Convergence

Problems

Chapter 3: Learning in Parametric Modeling: Basic Concepts and Directions

Abstract

3.1 Introduction

3.2 Parameter Estimation: The Deterministic Point of View

3.3 Linear Regression

3.4 Classification

3.5 Biased Versus Unbiased Estimation

3.6 The Cramér-Rao Lower Bound

3.7 Sufficient Statistic

3.8 Regularization

3.9 The ...