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Machine Learning

3.93 of 5 stars 3.93  ·  rating details  ·  278 ratings  ·  19 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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The Elements of Statistical Learning by Trevor HastiePattern Classification by Richard O. DudaPattern Recognition and Machine Learning by Christopher M. BishopMachine Learning by Tom M. MitchellInformation Theory, Inference and Learning Algorithms by David J.C. MacKay
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Machine Learning
11th out of 24 books — 19 voters

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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 ...more
Ivan Idris
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
Jul 13, 2011 Joecolelife rated it 5 of 5 stars
Recommended to Joecolelife by:
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
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.
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
Otto Hahaa
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.
Rohit Kumar
A good book for beginners.
Todd Johnson
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.
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.
Ondřej Sýkora
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).
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.
Alftheo Potgieter
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
Rodrigo Rivera
A real classic. Sadly, this book is already quite old. An updated edition would be greatly welcomed.
Rasoul Nasiri
A good introduction to machine learning, but I think it is not complete for learning machine learning.
Atul Dhingra
Pristinely presented algorithms, though it lacks the latest ML algorithms.
Santino Maguire
There is no math in this book. Why is there no math in this book!?
Lex Javier
I wish all textbooks were this enlightening and life-changing.
Hossein Kazemi
Needs to be updated.
Jan 22, 2010 DJ marked it as to-read
Shelves: computer-science
intro to machine learning
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