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Machine Learning

really liked it 4.0  ·  Rating Details ·  459 Ratings  ·  25 Reviews
Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.
Paperback, 414 pages
Published October 1st 1997 by McGraw-Hill (first published April 30th 1986)
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Popular Answered Questions

Rohit Vaidya Like Finite Automate. State transitions depend on probability based function. Can be used for gesture recognition.
I may be wrong ;)

Community Reviews

(showing 1-30)
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Ivan Idris
Jan 09, 2012 Ivan Idris rated it liked it
This is an introductory book on Machine Learning. There is quite a lot of mathematics and statistics in the book, which I like. A large number of methods and algorithms are introduced:

Neural Networks
Bayesian Learning
Genetic Algorithms
Reinforcement Learning

The material covered is very interesting and clearly explained. I find the presentation, however, a bit lacking. I think it has to do with the chosen fonts and lack of highlighting of important terms. Maybe it would have been better to have
...more
David
This is a very compact, densely written volume. It covers all the basics of machine learning: perceptrons, support vector machines, neural networks, decision trees, Bayesian learning, etc. Algorithms are explained, but from a very high level, so this isn't a good reference if you're looking for tutorials or implementation details. However, it's quite handy to have on your shelf for a quick reference.
Lurker
Dec 24, 2016 Lurker rated it really liked it
Shelves: cs-and-ai
Probably the first book you want in academic setting when studying machine learning. it's simple yet effective, and contains less mathematical mind-twisters and more concepts of machine learning algorithms.
Rajesh
Jan 24, 2017 Rajesh rated it it was amazing
Really good introductory content to machine learning, and a good reference for practitioners.
Orton
Used this for a course on computational learning. It's a classic in the field. It's not the place to start if you're trying to get models up and running. Mostly theory and if you look at the author's website most of the accompanying code is in C. With that said, Mitchell covers key material you need to keep in mind when choosing from the plethora of models out there to use.

For example, let's say you have your data and wanna classify new instances using two different algorithms and have them ma
...more
Chris
Feb 26, 2012 Chris rated it it was amazing
Machine Learning by Tom Mitchell was a good read that was surprisingly light on the math. It covered several different machine learning algorithms including: Concept Learning, Decision Tree, Neural Networks, Bayesian, Genetic Algorithms, Analytical Learning and Reinforcement Learning. It also mentions how to evaluate algorithms providing a training set limit equation and discussed how to evaluate hypothesis using confidence intervals. I enjoyed the structure and re-occurrence of specific concept ...more
Liuyang Li
Jan 10, 2016 Liuyang Li rated it liked it
The book does a good job summarizing various research areas in machine learning. It, however, lacks enough formal treatments. It also does not provide enough details for each topics so that the read can fully understand the algorithms to apply them. I would recommend reading this book as an overview of machine learning and read books on each individual topic afterwards.
Frank
Aug 04, 2015 Frank rated it really liked it
Shelves: coding-misc
This is a little harder than the Russell AI book, but it doesn't have the problem of not being complete. It doesn't try to give simple examples, but the math is complete, or complete enough for me. I didn't finish it all. It's more like a reference.
Randy
Dec 29, 2012 Randy rated it liked it
Shelves: quit-reading
Very clear and precise exposition. Pedagogical emphasis is entirely discrete: regular expressions, strings, grammars. WAY too much emphasis on formal CS theory, unless you're fascinated by computational learning theory. Not a suitable or intuitive choice for a first text in machine learning.
Todd Johnson
Mar 18, 2007 Todd Johnson rated it really liked it  ·  review of another edition
Shelves: machine-learning
Very clear prose. Covers an interesting sample of both probabilistic and non-probabilistic methods. Starting to feel a bit dated, as it does not cover important methods developed over the last decade, such as support vector machines. Nonetheless, the topics covered are covered very well.
Rodrigo Rivera
Sep 15, 2013 Rodrigo Rivera rated it it was amazing
A real classic. Sadly, this book is already quite old. An updated edition would be greatly welcomed.
Otto Hahaa
Oct 02, 2014 Otto Hahaa rated it really liked it
I liked this a lot when I read it ten years ago, but I am not so sure that you should read it now. A new edition would be nice.
Alftheo Potgieter
May 24, 2011 Alftheo Potgieter rated it liked it
A great read. I found it hard to study as is is not structured as a handbook. It also feels dated but it taught me quite a lot
Akash Raj
Jun 08, 2016 Akash Raj rated it really liked it
This book gives an introduction to machine learning and in simple terms. A great place to start machine learning
Darin
This edition of this book has become somewhat dated, but this is still my favorite machine learning book. However, I think a newer edition of the book is in the works so look for it.
Arioua Abdallah
Jan 02, 2013 Arioua Abdallah rated it really liked it
Clear and great book.
This book is an introductory material for any Artificial Intelligent course.
It presents the basic notions of machine learning in a structured way, with a clear explanation.
Ondrej Sykora
This book provides a solid foundation, though a more recent book might be a better choice now.

Moreover, I had problems with focusing while reading it (which does not happen to me as often).
Rasoul Nasiri
A good introduction to machine learning, but I think it is not complete for learning machine learning.
W
W rated it really liked it
Feb 02, 2009
Kijung Shin
Kijung Shin rated it it was amazing
Mar 02, 2013
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Oct 04, 2015
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Jul 01, 2010
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Apr 03, 2015
Hossein Kazemi
Sep 27, 2011 Hossein Kazemi rated it it was amazing
Needs to be updated.
Henrik Lindberg
Henrik Lindberg rated it it was amazing
Oct 18, 2012
Dan
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Dec 31, 2014
Michael Boegl
Michael Boegl rated it it was amazing
Jan 01, 2017
Benjamin Prosnitz
Benjamin Prosnitz rated it it was ok
Nov 28, 2010
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Ionut Hulub rated it it was amazing
Jul 31, 2012
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Stephan rated it liked it
Apr 13, 2011
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