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Machine Learning Hardcover – Mar 1 1997
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About the Author
THOMAS G. MITCHELL is the author of "Indispensable Traitors" (Greenwood, 2002) and "Native vs Settler" (Greenwood, 2000). His research concentration has been on ethnic conflicts in settler societies. He has also served with the Army in Bosnia and Kosovo.
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Top Customer Reviews
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.
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 For many purposes though it may have been superceded by R&N 2nd ed.
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.
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
Most recent customer reviews
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. Read morePublished on Jan. 8 2004 by Norraseth Chantasut
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. Read morePublished on Dec 4 2003 by Derek W. Hoiem
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... Read morePublished on June 21 2003 by Daniel
This book is a good introduction to the field, but I think the notation can be quite cumbersome at times. Read morePublished on April 24 2003 by Kelly Leahy
Excellent book , state-of-the-art, nice presentation and covering lots of topics in a friendly manner. highly recommended !Published on March 14 2003
I am a graduate student at a major research university. I am currently taking my fifth AI/Machine Learning graduate course. Read morePublished on Jan. 25 2003
Mitchell provides a good coverage of ML subject matter but niether goes right in-depth, nor gives a digestable overview. Read morePublished on Sept. 9 2002 by Nicholas Hynes
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