Signals, Systems and Inference

Course Description

This course 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. — Course material by Prof. George Verghese, MIT OpenCourseWare, used under CC BY-NC-SA 4.0. Original: https://ocw.mit.edu/courses/6-011-signals-systems-and-inference-spring-2018/