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MATH 474  Information Theory  Units: 3.00  
Topics include: information measures, entropy, mutual information, modeling of information sources, lossless data compression, block encoding, variable-length encoding, Kraft inequality, fundamentals of channel coding, channel capacity, rate-distortion theory, lossy data compression, rate-distortion theorem. Given jointly with MATH 874.
Learning Hours: 140 (36 Lecture, 104 Private Study)  
Requirements: Prerequisite STAT 268/3.0 or STAT 252/3.0. Recommended STAT 353/3.0.  
Offering Faculty: Faculty of Arts and Science  

Course Learning Outcomes:

  1. Computing Shannon's information measures (entropy, Kullback-Leibler distance and mutual information).
  2. Computing the capacity of communication channels.
  3. Reasoning about the properties of Shannon's information measures (entropy, Kullback-Leibler distance and mutual information).
  4. Using mathematical tools to infer properties of coding and communication systems.
  5. Working with probabilistic modeling of communication systems for source and channel coding purposes.
  6. Using tools from probability theory to analyze communication systems.
  7. Working with metric assessment of data compression code designs.