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Cambridge University Press Cambridge Information Theory: From Coding to Learning Book

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Description

Master the fundamentals of information theory with this comprehensive textbook from Cambridge University Press. This guide builds a strong foundation by taking readers from classical Shannon theory through to modern applications in statistical learning. It is designed to equip students with a rigorous understanding of core topics including data compression, channel coding, and rate-distortion theory. Using a unique finite block-length approach, the text bridges the gap between traditional communication theory and contemporary fields like machine learning and statistics. Whether you are studying engineering or advanced mathematics, this book provides the essential tools needed for further academic and professional study in the field.

Key Features

Cambridge University Press provides a rigorous foundation covering classical Shannon theory and modern statistical learning applications.

Study core topics including data compression, channel coding, and rate-distortion theory using a unique finite block-length approach.

Develop practical skills with over 210 end-of-part exercises designed to reinforce learning and concepts.

Explore contemporary applications in machine learning, statistics, and modern communication theory through numerous examples.

This textbook offers a well-rounded introduction for students pursuing advanced studies in engineering and technology.

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