Introduction to Machine Learning (Adaptive Computation and Machine Learning) (Hardcover)

by Ethem Alpaydin (Author)

Editorial Reviews

Product Description
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, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. 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. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book 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.

After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

About the Author
Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul.

Product Details

• Hardcover: 445 pages
• Publisher: The MIT Press (October 1, 2004)
• Language: English
• ISBN-10: 0262012111
• ISBN-13: 978-0262012119
• Product Dimensions: 9 x 8.1 x 1 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 4.4 out of 5 stars See all reviews (8 customer reviews)
• Amazon.com Sales Rank: #310,312 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

17 of 17 people found the following review helpful:

4.0 out of 5 stars Superb Organization of Ideas!, November 17, 2006

By	Machine Learner (Los Alamos, NM) - See all my reviews

The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all the ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.g. the r vector is used to denote the label vector and superscripts are used sometimes as subscripts. Second, the explanations are sometimes too brief. For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect!

12 of 13 people found the following review helpful:

4.0 out of 5 stars Good one to start, December 14, 2005

By	Subrat Nanda "Subrat" - See all my reviews

I would like to congratulate the author on writing this book, which is crisp and covers whole range of topics. What I liked the most is a systematic disucssion on a wide variety of areas in machine learning with a certain degree of details.

But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.

At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.

4 of 4 people found the following review helpful:

4.0 out of 5 stars Good overview of the field, March 30, 2008

By	M. A. Covington "M A Covington" (Athens, GA United States) - See all my reviews (REAL NAME)

I bought this for use as a reference book rather than a textbook. I found it quite useful with just one proviso: the mathematical presentation goes very fast in places and may be too concise for some readers.