Information Theory, Inference and Learning Algorithms
Author :
David J. C. MacKay
ISBN : 0521642981
Pages : 550
Publication Date : March 2005
Publisher : Cambridge University Press
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| David J. C. MacKay wrote: |
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and inference, often taught separately, are here united in one textbook. These topics lie at the heart of many areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.
This textbook 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. A toolbox of inference techniques, including message-passing 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 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.
Intended Audience:
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a first or second year undergraduate course on mathematics for scientists and engineers.
Review:
Amazon.com
"If you want to know what's presently going on in the field of coding theory with solid technical foundation, this is the book."
"For a course I help teach, the intoductions to probability theory and information theory save a lot of work."
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