Information theory concerns the fundamental limits for data compressibility and the rate at which data may be reliably communicated over a noisy channel. Course topics include measures of information, entropy, mutual information, Markov chains, source coding theorem, data compression, noisy channel coding theorem, error-correcting codes, and bounds on the performance of communication systems. Classroom discussion and homework assignments will emphasize fundamental concepts, and advanced topics and practical applications (e.g., industry standards, gambling/finance, machine learning) will be explored in group and individual research projects.
Course Prerequisite(s)
EN.525.614 Probability and Stochastic Processes for Engineers or equivalent.
Course Offerings
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Open
Information Theory
01/20/2026 - 04/27/2026
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