This course provides an introduction to the theory and algorithms of stochastic signals and their applications to the real world. Discrete random variables, random vectors, stochastic processes, and random fields are introduced followed by frequently encountered random processes including the white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields. Current methods used in statistical signal processing including detection, estimation, and optimal filtering, are covered. Special emphasis is given to Wiener and Kalman filters. Advanced topics in modern statistical signal processing such as linear prediction, linear models and spectrum estimation are also discussed.

• Section A: Mondays and Fridays, 11:00am - 12:00pm, CSE 111