Estimation Theory

3 credits · Graduate Programs

Fundamentals of estimation theory as applied to general statistical signal processing applications such as communication systems, image processing, target and position tracking, and machine learning. Estimator fundamentals including probability density functions, Cramer Rao bounds, Fisher information, linear and nonlinear regression, sufficient statistics, maximum likelihood estimation, minimum mean square error, least squares, Bayesian estimators and concepts. Statistical tracking filters such as Kalman filter and particle filter. Prerequisite(s): Admission to the PhD in Electrical and Computer Engineering or the MSc in Electrical Engineering.