Information Theory, Inference, and Learning Algorithms
Год выпуска: 2005
Автор: David J.C. MacKay
Издательство: Cambridge University Press
Формат: PDF
Качество: eBook (изначально компьютерное)
Количество страниц: 640
Язык: Английский
Описание: Описание на языке оригинала:
This book is aimed at senior undergraduates and graduate students in Engi-
neering, 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.
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 neural
networks.
Why unify information theory and machine learning? Because they are
two sides of the same coin. In the 1960s, a single field, cybernetics, was
populated by information theorists, computer scientists, and neuroscientists,
all studying common problems. Information theory and machine learning still
belong together. Brains are the ultimate compression and communication
systems. And the state-of-the-art algorithms for both data compression and
error-correcting codes use the same tools as machine learning.
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