David MacKay
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Information Theory, Inference, and Learning Algorithms



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Full blurb from back cover

Alternate blurbs

Jacket blurb (215 words)

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.

old jacket blurb

Information theory, probabilistic reasoning, coding theory and algorithmics lie at the heart of some of the most exciting areas of contemporary science and engineering. They are integral to such areas as communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. David MacKay breaks new ground in this exciting and entertaining textbook by introducing mathematical technology in tandem with applications, providing at once both motivation and hands-on guidance for problem-solving and modelling. For example, he covers not only the theoretical foundations of information theory, but practical methods for communication systems, including arithmetic coding for practical data compression, and low-density parity-check codes for reliable communication over noisy channels. The duality between communication and machine learning is illustrated through data modelling and compression; machine learning is also represented by clustering, classification, feedforward networks, Hopfield networks, Boltzmann machines and independent component analysis. A toolbox of probabilistic techniques are covered in detail: message-passing, Monte Carlo, and variational methods. The final part of the book, on sparse graph codes, describes the state-of-the-art in error-correcting codes, including chapters on low-density parity-check codes, turbo codes, and digital fountain codes. There are over 390 exercises, some with full solutions, which, together with worked examples, extend the text and enhance technique and understanding. A profusion of illustrations enliven and complement the text. Interludes on crosswords, evolution, and sex provide entertaining glimpses on unconventional applications. In sum, this is a textbook for courses in information, communication and coding for a new generation of students, and an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, data mining, financial engineering and machine learning.

150 word blurb

Information theory, probability, coding and algorithmics lie at the heart of some of the most dynamic areas of contemporary science and engineering. David MacKay breaks new ground in this exciting and entertaining textbook by introducing mathematical technology in tandem with applications, providing simultaneously both motivation and hands-on guidance for problem-solving and modelling. For example, he covers the theoretical foundations of information theory, and practical methods for communication systems. Communication and machine learning are linked through data modelling and compression. Over 390 exercises, some with full solutions, and nearly 40 worked examples, extend the text and enhance technique and understanding. Enlivening and enlightening illustrations abound. In sum, this is a textbook for courses in information, communication and coding for a new generation of students, and an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, data mining, financial engineering and machine learning.

90 word blurb

Information theory, probabilistic reasoning, coding theory and algorithmics underpin contemporary science and engineering. David MacKay breaks new ground in this exciting and entertaining textbook by introducing mathematics in tandem with applications. Over 390 exercises, some with full solutions, extend the text and enhance technique and understanding. Enlivening and enlightening illustrations abound. It's ideal for courses in information, communication and coding for a new generation of students, and an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, datamining, financial engineering and machine learning.

50 word blurb

This exciting and entertaining textbook is ideal for courses in information, communication and coding for a new generation of students, and an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, datamining, financial engineering and machine learning.

Another old short blurb

This textbook offers comprehensive coverage of Shannon's theory of information as well as the theory of neural networks and probabilistic data modelling. It includes explanations of Shannon's important source encoding theorem and noisy channel theorem as well as descriptions of practical data compression systems. Many examples and exercises make the book ideal for students to use as a class textbook, or as a resource for researchers who need to work with neural networks or state-of-the-art error-correcting codes.

Yet another old blurb

This textbook offers comprehensive coverage of Shannon's theory of information as well as the theory of neural networks and probabilistic data modeling. Shannon's source coding theorem and noisy channel theorem are explained and proved. Accompanying these theoretical results are descriptions of practical data compression systems including the Huffman coding algorithm and the less well known arithmetic coding algorithm. The treatment of neural networks is approached from two perspectives. On the one hand, the information-theoretic capabilities of some neural network algorithms are examined, and on the other hand, neural networks are motivated as statistical models. With many examples and exercises, this book is ideal for students to use as the text for a course, or as a resource for researchers who need to work with neural networks or state-of-the-art error correcting codes.

Site last modified Sun Aug 31 18:51:08 BST 2014