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At the end of this course students will have learned some of the state-of-the-art results in information theory and coding theory, and will have learned how to use the tools of these fields to identify fundamental limits of specific information processing tasks, and design of algorithms to attain these limits. This will enable students to emulate similar goals for other, more general information processing tasks.

This is intended as an advanced course in information theory and channel coding with coverage of topics not covered in the introductory information theory and coding theory courses. (Though there will be initial, brief, crash courses in information theory, and coding theory, for students who have not taken those, to make this course self-contained.) A tentative plan of topics is: 1. A self-contained crash course in Information Theory 2. A self-contained crash course in Coding Theory 3. Network information theory (selected topics from https://arxiv.org/abs/1001.3404v4) 4. Topics in coding theory (codes on graphs, polar codes, group testing, network coding, plus possibly other topics)

It would be helpful (but not essential) to have taken courses in information theory and/or channel coding. Knowledge of basic probability, linear algebra, and combinatorics (at the level of standard first-year undergraduate courses) will be expected.

4 slots of 45 minutes a week, of which 2-3 to 3 will be lectures, and 1-2 will be tutorials.

Homework and final exam.

Lecture notes will be provided.