6.011 Signals, Systems and Inference
Covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.
Lecture occurs 11:00 AM to 12:00 PM on Mondays and Wednesdays in 32-123.
This class counts for a total of 12 credits.
You can find more information on MIT OpenCourseWare at the Introduction to Communication, Control, and Signal Processing site or on the 6.011 Stellar site.
© Copyright 2015 Yasyf Mohamedali