Introduction to Communication, Control, and Signal Processing

This course examines signals, systems and inference as unifying themes 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; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiene…

The syllabus, on MIT OpenCourseWare

The full course — syllabus, assigned readings, problem sets, exams, and lecture notes — lives on OCW. These open the real thing: