Information Theory, Inference, and Learning Algorithms
by David J. C. MacKay
Publisher: Cambridge University Press 2003
ISBN/ASIN: 0521642981
ISBN-13: 9780521642989
Number of pages: 640
Description:
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.
Download or read it online for free here:
Download link
(multiple formats)
Similar books
From Classical to Quantum Shannon Theoryby Mark M. Wilde - arXiv
The aim of this book is to develop 'from the ground up' many of the major developments in quantum Shannon theory. We study quantum mechanics for quantum information theory, we give important unit protocols of teleportation, super-dense coding, etc.
(8001 views)
Conditional Rate Distortion Theoryby Robert M. Gray - Information Systems Laboratory
The conditional rate-distortion function has proved useful in source coding problems involving the possession of side information. This book represents an early work on conditional rate distortion functions and related theory.
(7015 views)
Information Theory and Codingby John Daugman - University of Cambridge
The aims of this course are to introduce the principles and applications of information theory. The course will study how information is measured in terms of probability and entropy, and the relationships among conditional and joint entropies; etc.
(19624 views)
The Limits of Mathematicsby Gregory J. Chaitin - Springer
The final version of a course on algorithmic information theory and the epistemology of mathematics. The book discusses the nature of mathematics in the light of information theory, and sustains the thesis that mathematics is quasi-empirical.
(9988 views)