Introduction to Machine Learning

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MIT Press, 2010 - Computers - 537 pages
6 Reviews

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.

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Review: Introduction to Machine Learning

User Review  - Rodrigo Rivera - Goodreads

Very decent introductory book. It gives a very broad overview of the different algorithms and methodologies available in the ML field. Each chapter reads almost independently. It is similar to the Mitchell book but more recent and slightly more math intensive. Read full review

Review: Introduction to Machine Learning

User Review  - Rodrigo Rivera - Goodreads

Very decent introductory book. It gives a very broad overview of the different algorithms and methodologies available in the ML field. Each chapter reads almost independently. It is similar to the Mitchell book but more recent and slightly more math intensive. Read full review

Contents

Introduction
1
Supervised Learning
21
Bayesian Decision Theory
47
Copyright

19 other sections not shown

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About the author (2010)

Ethem Alpaydin is a Professor in the Department of Computer Engineering at Bogaziši University, Istanbul.

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