Information Theory, Inference and Learning Algorithms

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  • Engels
  • Hardcover
  • 9780521642989
  • 25 september 2003
  • 640 pagina's
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David J.C. MacKay

David MacKay is a Professor in the Department of Physics at the University of Cambridge. He studied Natural Sciences at Cambridge and then obtained his PhD in Computation and Neural Systems at the California Institute of Technology. He returned to Cambridge as a Royal Society research fellow at Darwin College. He is internationally known for his research in machine learning, information theory, and communication systems, including the invention of Dasher, a software interface that enables efficient communication in any language with any muscle. He has taught Physics in Cambridge since 1995. Since 2005, he has devoted much of his time to public teaching about energy. He is a member of the World Economic Forum Global Agenda Council on Climate Change.

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This textbook covers Shannon's theory of information as well as neural networks and probabilistic data modelling.



Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book 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. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art 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 solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

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Inhoud

Taal
en
Bindwijze
Hardcover
Oorspronkelijke releasedatum
25 september 2003
Aantal pagina's
640
Illustraties
Met illustraties

Betrokkenen

Hoofdauteur
David J. C. MacKay
Tweede Auteur
David J.C. MacKay
Co Auteur
Mackay David J. C.
Hoofduitgeverij
Cambridge University Press

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Product breedte
195 mm
Product hoogte
34 mm
Product lengte
254 mm
Studieboek
Ja
Verpakking breedte
210 mm
Verpakking hoogte
40 mm
Verpakking lengte
264 mm
Verpakkingsgewicht
1846 g

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EAN
9780521642989

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