An Introduction to Single-User Information Theory (Springer Undergraduate Texts in Mathematics and Technology)

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