This course introduces students to discrete and continuous-time random processes. Correlation and power spectral density functions. Linear systems driven by random processes. Optimum detection and estimation. Bayesian, Weiner, and Kalman filtering. The stochastic processes that will be developed for the modelling and analysis of systems including: Markov Chains and Processes; Point Processes: Brownian Motion; and Martingales.