Machine Learning (Hardcover)

~ (Author) "Ever since computers were invented, we have wondered whether they might be made to learn..." (more)
Key Phrases: observed training examples, training derivatives, perfect domain theory, Morgan Kaufmann, International Conference, New York (more...)

Editorial Reviews

Product Description

This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Product Details

• Hardcover: 432 pages
• Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (March 1, 1997)
• Language: English
• ISBN-10: 0070428077
• ISBN-13: 978-0070428072
• Product Dimensions: 9.3 x 6.5 x 0.8 inches
• Shipping Weight: 1.5 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars  See all reviews (38 customer reviews)
• Amazon.com Sales Rank: #452,112 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

53 of 53 people found the following review helpful:

5.0 out of 5 stars An excellent overview for the adv. undergrad or beg. grad, September 30, 2002

By	Todd Ebert (Long Beach California) - See all my reviews

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

32 of 33 people found the following review helpful:

3.0 out of 5 stars Venerable, in both senses, April 4, 2004

By	eldil (Albuquerque NM) - See all my reviews

It's pretty well done, it covers theory and core areas but - maybe it was more the state of the field when it was written - I found it unsatisfyingly un-synthesized, unconnected, and short of detail (but this is subjective). I found the 2nd edition of Russell and Norvig to be a better introduction where it covers the same topic, which it does for everything I can think of, except VC dimension.

The book sorely needs an update, it was written in 1997 and the field has moved fast. A comparison with Mitchell's current course (materials generously available online) shows that about 1/4 of the topics taught have arisen since the book was published; Boosting, Support Vector Machines and Hidden Markov Models to name the best-known. The book also does not cover statistical or data mining methods.

Despite the subjective complaint about lack of depth it does give the theoretical roots and many fundamental techniques decently and readably. For many purposes though it may have been superceded by R&N 2nd ed.

24 of 25 people found the following review helpful:

5.0 out of 5 stars An excellent textbook for machine learning, January 26, 2001

By	Ernest Davis "davise9" (New York, NY USA) - See all my reviews

In fall 2000, I taught a master's level course in ML to about 25 students at New York University. Fortunately both for me and my students, I was able to use and assign excellent recent textbooks in the area: "Machine Learning" by Tom Mitchell and "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations" by Ian H. Witten and Eibe Frank. I recommend both books enthusiastically.

A student who has mastered Mitchell has a solid grasp of the basic element of nearly every method of machine learning currently in use, and of almost every aspect of ML research. A student who has mastered Witten/Frank has a deep knowledge of the major ML techniques, and a strong sense of the opportunities and pitfalls to be encounted when these techniques are put into practice....