Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of 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 text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already,including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of 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 text covers such topics as supervised learning, Bayesian decision theory,parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments;case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming,probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

Contents......Page 7 Series Foreword......Page 17 Figures......Page 19 Tables......Page 29 Preface......Page 31 Acknowledgments......Page 33 Notes for the Second Edition......Page 35 Notations......Page 39 1 Introduction......Page 41 2 Supervised Learning......Page 61 3 Bayesian Decision Theory......Page 87 4 Parametric Methods......Page 101 5 Multivariate Methods......Page 127 6 Dimensionality Reduction......Page 149 7 Clustering......Page 183 8 Nonparametric Methods......Page 203 9 Decision Trees......Page 225 10 Linear Discrimination......Page 249 11 Multilayer Perceptrons......Page 273 12 Local Models......Page 319 13 Kernel Machines......Page 349 14 Bayesian Estimation......Page 381 15 Hidden Markov Models......Page 403 16 Graphical Models......Page 427 17 Combining Multiple Learners ......Page 459 18 Reinforcement Learning......Page 487 19 Design and Analysis of Machine Learning Experiments......Page 515 A Probability......Page 557 Index......Page 569 I've read several parts of chapters which concerned my work and skimmed other chapters faster. This book should serve as a starting point and mostly as a quick introduction in a subject. However, i've found this book to be useful in other way - it is compact and I found several basic reasonements and assumptions quickly to base my conclusions in work i was doing. Also i like the style where key-words appear outside the text where they can be easily spotted and also the references at the end of each chapter.