Information Theory, Inference and Learning Algorithms

A textbook on information, communication, and coding for a new generation of students, and an entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

**Tag(s):**
Information Theory

**Publication date**: 01 Mar 2005

**ISBN-10**:
0521642981

**ISBN-13**:
n/a

**Paperback**:
550 pages

**Views**: 23,542

Information Theory, Inference and Learning Algorithms

A textbook on information, communication, and coding for a new generation of students, and an entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Terms and Conditions:

Book excerpts:

Information theory 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

:smile: "If you want to know what's presently going on in the field of coding theory with solid technical foundation, this is the book."

:smile: "For a course I help teach, the intoductions to probability theory and information theory save a lot of work."

David J. C. MacKay wrote:Now the book is published, these files will remain viewable on this website. The same copyright rules will apply to the online copy of the book as apply to normal books. [e.g., copying the whole book onto paper is not permitted.]

Book excerpts:

Information theory 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

:smile: "If you want to know what's presently going on in the field of coding theory with solid technical foundation, this is the book."

:smile: "For a course I help teach, the intoductions to probability theory and information theory save a lot of work."

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About The Author(s)

David J. C. MacKay is Regius Professor of Engineering at Cambridge University Engineering Department. He works on Bayesian probability theory and its application to inference problems.

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