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Information Theory, Inference and Learning Algorithms

By: David J. C. MacKay
Binding: Hardcover
Publisher: Cambridge University Press
ISBN: 0521642981
ISBN-13: 9780521642989
Released: 25 Sep 2003
RRP: £35.00
Average Rating:

Customer Reviews

pretty much indispensible - By: S. Matthews, 26 Sep 2008
This is an unqualified classic, to shelve with the likes of 'Structure & Interpretation of Computer Programs', 'Concrete Mathematics' & 'Mathematical Methods of Classical Mechanics'. If you are involved with, or interested in, high-end data analytics, then you _need_ this.

However 'high-end data analytics' does not even begin to do the book justice, so let me try again.

This is a magnificent compendium of fascinating stuff presented in a coherent information-theoretic framework. It covers everything from how digital television data compression & CD error correction work to a detailed commentary on neural networks, & discussion of principled AI methods such as clustering, Gaussian processes & probabilistic graphical models, together with Monte-Carlo techniques & a bunch of statistical physics. It even throws in a complete course in Bayesian statistics. It reads like a reallly good 'popular' 'science' book (I often wonder where the scare quotes should be) that doesn't bother to try to be popular.

In fact I bought this originallly as bedside reading, for pleasure. It was only later that I actuallly used it for anything.
Fun packed, information packed, but uncluttered. - By: Rich Turner, 09 Mar 2005
Uniting information theory & inference in an interactive & entertaining way, this book has been a constant source of inspiration, intuition & insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.

This is _not_ just a book for the experts. However, you will need to think & interact when reading it. That is, after alll, how you learn, & the book helps & guides you in this with many puzzles & problems.
Excellent book on inference and learning ... - By: Jurgen Van Gael, 21 Nov 2003
I have been able to use this book as extra background material for several courses of my final undergraduate year.

First I have been able to find a lot of usefull information on coding theory. Although this book isn't meanth to be a treatise on several coding, decoding techniques it gives the reader a lot of insight in the connection between coding & information theory. You won't find how matrix decoding algorithms, cyclic codes etc work but you will find out how the limits of information theory restrict coding theory.

I cannot compare the information theoretic approach to any other book as this was my first introduction but I can say the information theoretic treatise was a good read & I make myself strong I now have a solid information theory background.

Another course for which I have been able to use this book was a course on uncertainty reasoning. Mckay's book covers inference in great depth & introduces the reader to several different area's such as belief networks, decision theory, bayesian networks & several other inference methods. As before I cannot compare the ising, monte carlo like methods but it did give me a good introduction. Concerning the bayesian probability/inference, decision theory I can only say this is THE best introduction I have read!

I have read several introductions on Neural Networks (Kevin Geurny). This book keeps up with the standard set by several other good introductions.

Inference/Learning is a vast research area & this books gives a good introduction in alll areas. Even as the part on neural networks may be as good as some other books on the topic I would definitely advise this book as for the same price you get so much more introductions to other learning techniques. The last thing which I like very much is the fact that several excercies are solved or come with hints which makes it for a student a very good book accompanying other courses. The author has a very clear writing style & knows when to add a good joke to make the reading more enjoyable.

My conclusion: if you are an undergraduate student interested in learning & inference -> "Go get this book asap!!!"