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 Title Information Theory, Inference and Learning Algorithms
 Author(s) David J. C. MacKay
 Publisher: Cambridge University Press, 1st Ed. (October 6, 2003); eBook (4th printing, March 2005)
 Hardcover/Paperback 640 pages
 eBook Multiple Formats: Postscript, PDF, DJVU, etc.
 Language: English
 ISBN10/ASIN: 0521642981
 ISBN13: 9780521642989
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Book Description
Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering  communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. financial engineering, and machine learning.
This textbook introduces Information theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparsegraph codes for errorcorrection.
A toolbox of inference techniques, including messagepassing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.
The final part of the book describes the state of the art in errorcorrecting codes, including lowdensity paritycheck codes, turbo codes, and digital fountain codes  the twentyfirst century standards for satellite communications, disk drives, and data broadcast.
Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for selflearning and for undergraduate or graduate courses.
Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology.
About the Authors David MacKay is a professor in the department of physics at Cambridge University, a member of the World Economic Forum's Global Agenda Council on Climate Change, and a regular lecturer on sustainable energy.
 Information Theory and Systems
 Machine Learning
 Algorithms and Data Structures
 Combinatorics and Game Theory
 Discrete and Finite Mathematics
 Operations Research and Optimization
 Computational Complexity
 Information Theory, Inference and Learning Algorithms (David J. C. MacKay)
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