Signals, Systems and Inference
MIT's course on signals, systems, and inference covers the mathematical tools linking 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. The course covers state feedback and observers, probabilistic models, stochastic processes, correlation functions, and power spectra, with spectral factorization. It also treats least-mean-square error estimation and Wiener filtering, plus hypothesis testing, detection, and matched filters. Materials come from MIT OpenCourseWare and typically include lecture notes, problem sets, and exams used in MIT's electrical engineering curriculum. The course suits students who already have a background in linear systems and probability and want to see how those tools combine to estimate signals buried in noise and detect signals against competing hypotheses.