Outline: entropy and mutual information, the asymptotic equipartition principle, entropy for stochastic processes (entropy rate), introduction to data compression and source coding, channel capacity and coding for noisy channels, capacity for different channel models (with emphasis on discrete memoryless channels and Gaussian channels), finite field theory, design and analysis of error correcting codes (with a focus on linear block codes), introduction to network information theory
Format: Teaching the course will be based on one meeting, or seminar, per week (with about 12 meetings total, for the complete doctoral student version). The examination of the course will be based on: active participation, homework problems and, for the doctoral student version (see below), presentation/review of an article in the field. The overall emphasis is on individual off-class problem solving, based on relatively demanding homework problems. More information about these can be found here.
Two versions: The course is eligible for both undergraduate (2E5207, 5p) and doctoral (2E5316, 8p) students. The difference between the two versions of the course is in the extent and level of difficulty of the material included. With reference to the course schedule the senior undergraduate version, 2E5207, will amount to the material treated in meetings 1-8 while 2E5316 includes in addition the theoretically more demanding material corresponding to meetings 9-11 as well as a separate presentation/review of a research paper in the field.