• 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
• ISBN-10/ASIN: 0521642981
• ISBN-13: 978-0521642989
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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 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.

• David J. C. MacKay was a British physicist, mathematician, and academic.

• Amazon

• Information Theory and Systems
• Machine Learning
• Algorithms and Data Structures
• Combinatorics and Game Theory
• Discrete and Finite Mathematics
• Operations Research and Optimization
• Computational Complexity

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