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Machine Learning [Hardcover]

Thomas Mitchell
4.4 out of 5 stars  See all reviews (21 customer reviews)
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Book Description

March 1 1997 0070428077 978-0070428072 1
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.

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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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Customer Reviews

Most helpful customer reviews
3.0 out of 5 stars Technique Without Substance April 26 2004
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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3.0 out of 5 stars Venerable, in both senses April 4 2004
By eldil
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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5.0 out of 5 stars The good textbook for beginning research Jan. 9 2004
This textbook is useful too much for students that need to learn about learning algorithms. In this textbook explains the key theory and algorithms to solve various problems. For student who plan to make a research in machine learning, this textbooks can give basic knowledge and background of the research.
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5.0 out of 5 stars Excellent Introductory Text on ML Dec 4 2003
This book serves as an excellent introduction to machine learning. The material covers a broad range of the important concepts and methods used in machine learning today. The text is well-written and well-organized, and I find myself frequently using it as a reference.
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4.0 out of 5 stars The best book for machine learning June 22 2003
By Daniel
When I came to the field of machine learning, the book provides me a clear, easy-understanding picture to the field so that I believe any of you can get into the field by use of the book. If you are looking for your first book to this field, don't waste your time, it is. Even through my life in the research, I depended on it most of the time. It's too great.
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3.0 out of 5 stars So - so April 24 2003
This book is a good introduction to the field, but I think the notation can be quite cumbersome at times. I've seen the concepts presented elsewhere in less confusing form, but it's a good general source as it includes a considerable amount of information from relatively current research. The examples are typically very easy to understand, though they aren't always complicated enough to make the notation easy to understand.
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5.0 out of 5 stars excellent book March 14 2003
By A Customer
Excellent book , state-of-the-art, nice presentation and covering lots of topics in a friendly manner. highly recommended !
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5.0 out of 5 stars Only book of it's kind Jan. 25 2003
By A Customer
I am a graduate student at a major research university. I am currently taking my fifth AI/Machine Learning graduate course. This is the one book everyone grabs for when they need a reference. I had to mark the spine of my book with tape so I could find it more easily on my colleagues shelves.
Other books are either not as accessible or too niche-specific. This is the only book out there that covers all of the major machine learning techniques (with the possible exception of support vector machines) and covers them in a manner that can be well understood.
Every discipline has one book that must be on your shelf. If you are planning on doing serious research in Machine Learning - this is the one book.
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