The goal of machine learning is to program computers to use example data or past experience to solve a given problem.Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The new edition incorporates three topics ??" namely, kernel methods, Bayesian estimation, and graphical models in detail. A chapter on statistical test is rewritten as one that includes the design and analysis of machine learning. The book is intended for senior graduate and postgraduate level courses on machine learning. It should also be of great interest to engineers working in the field concerned with the application of machine learning methods. ???This volume offers a very accessible introduction to the field of machine learning. Ethem Alpaydin gives a comprehensive exposition of the kinds of modeling and prediction problems addressed by machine learning, as well as an overview of the most common families of paradigms, algorithms, and techniques in the field. The volume will be particularly useful to the newcomer eager to quickly get a grasp of the elements that compose this relatively new and rapidly evolving field.??? ??" Joaquin Qui??onero-Candela, Coeditor, Dataset Shift in Machine Learning Table Of Contents: CONTENTS 1. Introduction 2. Supervised Learning 3. Bayesian Decision Theory 4. Parametric Methods 5. Multivariate Methods 6. Dimensionality Reduction 7. Clustering 8. Nonparametric Methods 9. Decision Trees 10. Linear Discrimination 11. Multilayer Perceptrons 12. Local Models 13. Ke