Book:Information Theory, Inference, and Learning Algorithms
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[edit] Bibliographical Data
| Title: | Information Theory, Inference, and Learning Algorithms
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| Author: | David MacKay
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| Subjects: | Computer Science
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| Key words: |
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| Education Level: |
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| License: | All Rights Reserved - Standard Copyright
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[edit] Abstract
From the book jacket:
- 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 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, the book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.
- The result 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, financial engineering, and machine learning.
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