Information Theory, Inference, and Learning Algorithms
Book • 2002
This book unifies information theory, inference, and learning algorithms, providing a richly illustrated and entertaining introduction to these subjects.
It covers topics such as data compression, error-correcting codes, Bayesian inference, and neural networks, making it ideal for self-learning and courses in engineering, science, mathematics, and computing.
The book includes over 400 exercises and detailed solutions, making it a valuable resource for both students and professionals.
It covers topics such as data compression, error-correcting codes, Bayesian inference, and neural networks, making it ideal for self-learning and courses in engineering, science, mathematics, and computing.
The book includes over 400 exercises and detailed solutions, making it a valuable resource for both students and professionals.
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Mentioned by Michael Douglas as a book illustrating the relevance of Bayesian methods and statistics in machine learning.
[33] Michael R. Douglas - G/H Conformal Field Theory
