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Information Theory, Inference and Learning Algorithms (Hardcover)

by David J. C. MacKay (Author) "In this chapter we discuss how to measure the information content of the outcome of a random experiment ..." (more)

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Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) by Christopher M. Bishop
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Product details

Hardcover: 550 pages
Publisher: Cambridge University Press; Sixth Printing 2007 edition (25 Sep 2003)
Language English
ISBN-10: 0521642981
ISBN-13: 978-0521642989
Product Dimensions: 26.6 x 19.6 x 3.6 cm

Average Customer Review: 5.0 out of 5 stars See all reviews (3 customer reviews)

Amazon.co.uk Sales Rank: 25,245 in Books (See Bestsellers in Books)

Popular in these categories:

#1 in	Books > Computing & Internet > Computer Science > Artificial Intelligence > Neural Networks
#2 in	Books > Computing & Internet > Computer Science > Information Systems > Information Theory
#2 in	Books > Science & Nature > Popular Science > Artificial Intelligence

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Review

'This is an extraordinary and important book, generous with insight and rich with detail in statistics, information theory, and probabilistic modeling across a wide swathe of standard, creatively original, and delightfully quirky topics. David MacKay is an uncompromisingly lucid thinker, from whom students, faculty and practitioners all can learn.' Peter Dayan and Zoubin Ghahramani, Gatsby Computational Neuroscience Unit, University College, London

‘This is primarily an excellent textbook in the areas of information theory, Bayesian inference and learning algorithms. Undergraduates and postgraduates students will find it extremely useful for gaining insight into these topics; however, the book also serves as a valuable reference for researchers in these areas. Both sets of readers should find the book enjoyable and highly useful.’ David Saad, Aston University

'An utterly original book that shows the connections between such disparate fields as information theory and coding, inference, and statistical physics.' Dave Forney, Massachusetts Institute of Technology

'An instant classic, covering everything from Shannon's fundamental theorems to the postmodern theory of LDPC codes. You'll want two copies of this astonishing book, one for the office and one for the fireside at home.' Bob McEliece, California Institute of Technology

‘… a quite remarkable work … the treatment is specially valuable because the author has made it completely up-to-date … this magnificent piece of work is valuable in introducing a new integrated viewpoint, and it is clearly an admirable basis for taught courses, as well as for self-study and reference. I am very glad to have it on my shelves.’ Robotica

‘With its breadth, accessibility and handsome design, this book should prove to be quite popular. Highly recommended as a primer for students with no background in coding theory, the set of chapters on error correcting codes are an excellent brief introduction to the elements of modern sparse graph codes: LDPC, turbo, repeat-accumulate and fountain codes are described clearly and succinctly.’ IEEE Transactions on Information Theory

Product Description

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

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In this chapter we discuss how to measure the information content of the outcome of a random experiment. Read the first page

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12 of 12 people found the following review helpful:

5.0 out of 5 stars Excellent book on inference and learning ..., 21 Nov 2003

By	Jurgen Van Gael (Cambridge, UK) - See all my reviews (REAL NAME)

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 and 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 and 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 and introduces the reader to several different area's such as belief networks, decision theory, bayesian networks and 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 and this books gives a good introduction in all 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 and knows when to add a good joke to make the reading more enjoyable.

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

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11 of 11 people found the following review helpful:

5.0 out of 5 stars Fun packed, information packed, but uncluttered., 9 Mar 2005

By	Rich Turner "Rich" (London England) - See all my reviews

Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and 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 and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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2 of 2 people found the following review helpful:

5.0 out of 5 stars pretty much indispensible, 26 Sep 2008

By	S. Matthews "Sean Matthews" (Mainz, Germany) - See all my reviews

This is an unqualified classic, to shelve with the likes of 'Structure and Interpretation of Computer Programs', 'Concrete Mathematics' and '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 and CD error correction work to a detailed commentary on neural networks, and discussion of principled AI methods such as clustering, Gaussian processes and probabilistic graphical models, together with Monte-Carlo techniques and a bunch of statistical physics. It even throws in a complete course in Bayesian statistics. It reads like a really 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 originally as bedside reading, for pleasure. It was only later that I actually used it for anything.

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