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Machine Learning Hardcover – Mar 1 1997


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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: 16.3 x 3.3 x 24.1 cm
  • Shipping Weight: 699 g
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (21 customer reviews)
  • Amazon Bestsellers Rank: #499,737 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Ever since computers were invented, we have wondered whether they might be made to learn. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews

4.4 out of 5 stars
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Most helpful customer reviews

Format: Hardcover
Have you ever bought something that warns "assembly required" only to find out that the instructions are missing? That's a pretty accurate analogy to how Mitchell's book left me feeling. With an empty smile, he hands you a bag of techniques and tells you to go to town. Sure, there are hints about when you might apply particular algorithms, but they are abstract and occasionally hidden in the text. It's as if you've been handed a wrench and told that it can turn things. Huzzah.
When describing a field of knowledge, it's important to communicate the "Big Picture." Mitchell does a poor job of this. That is to say that he doesn't do this at all. The lack of a pervasive thread is all the more odd and disconcerting given that his dissertation gave an amazingly coherent description of the process of inductive learning. I suppose I feel a bit taken because there's nothing so tangible or real to hold the disjoint chapters together. So, without any real historical or philosophical context, we're left with something reminiscent of a first-year calculus book. Here's how to differentiate, here's how to integrate, now go figure out what you're supposed to do with those things.
Nevertheless, anyone needing a reference guide (think of a shop manual) to machine learning techniques (that isn't quite up to date) would do well to buy this book. Anyone wanting to understand the field of machine learning should probably check out a bit of the competition. I think you'll find that some folks' kung fu is stronger.
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By eldil on April 4 2004
Format: Hardcover
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.
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Format: Hardcover
I got this book from the university's library, because I wanted a nice book that can show me different methods for machine learning, so I can learn the buzzwords, and understand their meaning, at least in principle. It was important to me that the book won't go too much in depth into any subject, and more importantly, that the book won't use unfamiliar terminology, unless explained before.
This book is indeed a nice overview of the field at the time it was written, although a lot happened in Machine Learning since, the book remains a good source for learning what can be done and how.
Reading the book is quite easy (I'm a graduate student in computer science), and quickly I got what I was asking for.
This book is not a magic pill for any problem. This is not a cookbook. So the compliments are in place as long as you know what you're looking for.
These days I attend a seminar where the students are asked, each student in turn to present one chapter from this book (and some other books). Surely, once the seminar is over, we (the students) will know enough to be able to chat about Machine Learning, and more importantly, we will know where to look for deeper texts if we wanted to.
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Format: 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
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