Basic Information
Shannon Information Measures, and Their Properties. The Interplay Between Information Theory And Extremal Combinatorics, With Applications. The Asymptotic Equipartition Property, and Lossless Compression Of Discrete Stationary Sources. Shannon Capacity of Graphs, Properties, and Bounds. The Method of Types in Combinatorics, With Applications in Information Theory. The Generalized Information Measures By Renyi, and The Guessing Problem. Generalized Divergence Measures With Applications. _learning Outcomes# at The End of The Course The Students Will Be Able To# 1. Familiarity With Classical and Generalized Information Measures, Together With Their Diverse Applications (especially in Combinatorics And Graph Theory). 2. Formulate New Problems and to Apply These Mathematical Tools In Solving Them.
Faculty: Mathematics
|Undergraduate Studies
|Graduate Studies
Pre-required courses
(44114 - Discrete Mathematics Ee and 104013 - Differential and Integral Calculus 2t and 104016 - Algebra 1/extended and 104034 - Introduction to Probability H) or (94412 - Probability (advanced) and 104032 - Calculus 2m and 104166 - Algebra Am and 234141 - Combinatorics For Cs) or (104066 - Algebra A and 104222 - Probability Theory and 104281 - Infinitesimal Calculus 2 and 104286 - Combinatorics)
Course with no extra credit (contained)
106718 - Information Theory in Combinatorics