This graduate-level course introduces students to detection and estimation theory, with applications to communications, control, imaging, and image processing. Topics include hypothesis testing; linear and non-linear estimation; maximum likelihood and Bayes approaches; stochastic processes and systems; signal detection and estimation in noise; decision-theory concepts; optimum-receiver principles and Markov chain Monte Carlo techniques.