Machine Learning (Mcgraw-Hill International Edit)

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	Machine Learning (Mcgraw-Hill International Edit)
	by Thomas Mitchell Sales Rank: 55373	$79.45 At Amazon on 10-28-2008.

*Features* Cover Type: Paperback with 352 pages Published by: McGraw Hill Higher Education Edition: 1st Edition October 1, 1997 Written in: English ISBN 10 Number: 0071154671 ISBN 13 Number: 978-0071154673 Book Dimensions: 9 x 5.9 x 1.1 inches Weighs: 1.2 pounds *Product Description* This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning--including probability and statistics, artificial intelligence, and neural networks--unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students. --This text refers to the Hardcover edition. *Reader Reviews* This review is from: Machine Learning (Hardcover) I agree with some of the previous reviews which criticize the book for its lack of depth, but I believe this to be an asset rather than a liability given its target audience (seniors and beginning grad. students). The average college senior typically knows very little about subjects like neural networks, genetic algorithms, or Baysian networks, and this book goes a long way in demystifying these subjects in a very clear, concise, and understandable way. Moreover, the first-year grad. student who is interested in possibly doing research in this field needs more of an overview than to dive deeply into one of the many branches which themselves have had entire books written about them. This is one of the few if only books where one will find diverse areas of learning (e.g. analytical, reinforcment, Bayesian, neural-network, genetic-algorithmic) all within the same cover. But more than just an encyclopedic introduction, the author makes a number of connections between the different paradigms. For example, he explains that associated with each paradigm is the notion of an inductive-learning bias, i.e. the underlying assumptions that lend validity to a given learning approach. These end-of-chapter discussions on bias seem very interesting and unique to this book. Finally, I used this book for part of the reading material for an intro. AI class, and received much positive feedback from the students, although some did find the presentation a bit too abstract for their undergraduate tastes Back To Top
Machine Learning (Mcgraw-Hill International Edit) ~~List Price: $79.45~~ Available from Amazon Price: $79.45 Updated on 10-28-2008.

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