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Information Theory, Inference, and Learning Algorithms by David J. C. MacKay & David J. C. Mac Kay is a science book available to read on EtoBox.

What is Information Theory, Inference, and Learning Algorithms about?

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. 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. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed s

Who reads Information Theory, Inference, and Learning Algorithms?

Author

David J. C. MacKay & David J. C. Mac Kay

Publisher

CAMBRIDGE UNIV PRESS; Cambridge University Press

Published

2003

Language

EN

ISBN

9781311335333

Category

science

Subjects

Science, Engineering, Programming

Rating

4.43 / 5 (256 ratings)

Updated

2026-03-14