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
There are no sections currently offered, however you can view a sample syllabus from a prior section of this course.