Signals, Systems and Inference

One noisy image of astronaut on the moon with flag above a fixed version of the same image.

Comparison of a noisy image of an astronaut (above) with the image after a Wiener filter is applied (below). Original image courtesy of NASA and is in the public domain. Noisy and filtered images courtesy of OCW. 


MIT Course Number


As Taught In

Spring 2018



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Course Description

Course Features

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.

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George Verghese, Alan Oppenheim, and Peter Hagelstein. 6.011 Signals, Systems and Inference. Spring 2018. Massachusetts Institute of Technology: MIT OpenCourseWare, License: Creative Commons BY-NC-SA.

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