Information and Coding Theory - Autumn 2017

TTIC 31200/CMSC 37220

M W 1:30-2:50, TTIC Room 526

Discussion: Th 4-5 pm, TTIC Room 530

Instructor: Madhur Tulsiani

TA: Mrinalkanti Ghosh


This course is meant to serve as an introduction to some basic concepts in information theory and error-correcting codes, and some of their applications in computer science and statistics. We plan to cover the following topics:

  • Introduction to entropy and source coding. Some applications of entropy to counting problems.
  • Mutual information and KL-divergence. Method of types and hypothesis testing.
  • I-projections, maximum entropy, exponential families and applications.
  • Introduction to error-correcting codes. Unique and list decoding of Reed-Solomon and Reed-Muller codes.
  • Applications of information theory to problems in theoretical computer science.

The course will have 4-5 homeworks (60 percent) and a final (40 percent).

There is no textbook for this course. A useful reference is ``Elements of Information Theory'' by T. M. Cover and J. A. Thomas. Also take a look at the resources section below.

Homeworks and Announcements

  • No lecture on Wednesday, October 4.
  • Homework 1 (Due on 10/16/17).

Lecture Plan and Notes

  • 9/25: Entropy of a random variable. Prefix-free codes and Kraft's inequality.
  • 9/27: Conditional and joint entropy. Subadditivity of entropy and combinatorial applications. Fundamental source coding theorem.
  • 10/2: Proof of Shearer's lemma. Some more combinatorial and number-theoretic applications of entropy. Mutual information.
  • 10/4: NO CLASS
  • 10/9: Data processing inequality, KL-divergence, total-variation distance, Pinsker's inequality.