Course Description

Information theory is a young branch of mathematics created to study digital data and digital communications. One problem is that of data compression: how to rewrite a digitally encoded message so that it occupies less physical media, such as disk space or memory? Another problem is that of error correction: given a lossy communication channel how to rewrite a digital message so that the message can be accurately transmitted with high probability? The answers to both problems revolve around ``Shannon entropy'', a single number based on the distribution of symbols in the message, which determines how efficiently both problems can be solved. This course aims at covering topics in probability theory, mathematical modeling and algebra of finite fields. It will be demonstrated how these branches of mathematics work together in solving problems in compression and error correction. Every aspect of the course will be illustrated with short programs written in MATLAB.

The topics covered will include:

  • Fundamentals of Data Compression.
  • Fundamentals of Error-Correcting Codes.
  • Relevant Topics of Applied Probability.
  • Relevant Topics in Finite Fields.