Книга Information Theory, Inference, and Learning Algorithms

Information Theory, Inference, and Learning Algorithms

Книги English литература

Автор:

David J.C. MacKay

Год издания: 2005

Формат: pdf

Издат.:

Cambridge University Press

Страниц: 640

Размер: 11 Мб

Язык: Английский

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Оценка:

Information Theory, Inference, and Learning Algorithms

Описание на языке оригинала:This book is aimed at senior undergraduates and graduate students in Engi-neering, Science, Mathematics, and Computing. It expects familiarity withcalculus, probability theory, and linear algebra as taught in a first- or second-year undergraduate course on mathematics for scientists and engineers. Conventional courses on information theory cover not only the beauti-ful theoretical ideas of Shannon, but also practical solutions to communica-tion problems. This book goes further, bringing in Bayesian data modelling,Monte Carlo methods, variational methods, clustering algorithms, and neuralnetworks. Why unify information theory and machine learning? Because they aretwo sides of the same coin. In the 1960s, a single field, cybernetics, waspopulated by information theorists, computer scientists, and neuroscientists,all studying common problems. Information theory and machine learning stillbelong together. Brains are the ultimate compression and communicationsystems. And the state-of-the-art algorithms for both data compression anderror-correcting codes use the same tools as machine learning.

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