This second edition is extensively updated to reflect the growing synergy between information theory and artificial intelligence (AI) . Key enhancements include:

• New Content Focused on AI & Machine Learning: A major new chapter introduces generalized information and statistical measures (such as parameterized Rényi, Jensen, and f-divergence), alongside expanded coverage of the information bottleneck principle , cross-entropies , and generalized information inequalities , all connected to modern ML applications like deep learning and generative models.
• Comprehensive Updates Throughout: All existing chapters have been revised and expanded , the appendix enhanced , and chapter problem sets significantly enlarged based on the first edition. The book presents a succinct and mathematically rigorous treatment of the main pillars of Shannon's information theory, discussing the fundamental concepts and indispensable results of Shannon's mathematical theory of communications. It includes six meticulously written core chapters (with accompanying problems), emphasizing the key topics of information measures (classical and generalized); lossless and lossy data compression; channel coding; and joint source-channel coding for single-user (point-to-point) communications systems. It also features two appendices covering necessary background material in real analysis and in probability theory and stochastic processes.
• Improved Accuracy & Resources: Errors from the first edition have been corrected, and the bibliography and index are thoroughly updated . A separate, 400-page instructor's solutions manual is also available.

The book is ideal for a one-semester foundational course on information theory for senior undergraduate and entry-level graduate students in mathematics, statistics, engineering, and computing and information sciences.

Fosters an in-depth understanding of the concepts covered Offers a concise discussion of the main pillars of Shannon's information theory Is ideal for a one-semester foundational course on information theory

Autorentext
Fady Alajaji is a professor of Mathematics and Engineering at the Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada. He received a B.E. degree with distinction from the American University of Beirut, Lebanon in 1988, and M.Sc. and Ph.D. degrees from the University of Maryland at College Park, USA, in 1990 and 1994, respectively.In 2001, he was a recipient of the Premier's Research Excellence Award from the Province of Ontario in recognition for his research in "the theory and practice of joint source-channel coding in telecommunication systems." His research interests include information theory, applied probability, joint source-channel coding, error control coding, data compression and digital communications. Po-Ning Chen is a professor at the Department of Electrical and Computer Engineering, National Chiao Tung University (NCTU), Taiwan. He received B.E. and M.Sc. degrees from National Tsing Hua University, Taiwan, in 1985 and 1987, respectively, and his Ph.D. degree from the University of Maryland at College Park, USA, in 1994. He was a recipient of the 2000 Young Scholar Paper Award from the Academia Sinica, Taiwan.He was also selected as the NCTU Outstanding Tutor Teacher in 2013 and 2014, and received the NCTU Distinguished Teaching Award in 2014. His research interests include information and coding theory, large deviations theory, distributed detection and sensor networks.

Inhalt
Introduction.- Information Measures for Discrete Systems.- Lossless Data Compression.- Data Transmission and Channel Capacity.- Dierential Entropy and Gaussian Channels.- Lossy Data Compression and Transmission.