System Identification & Learning

3 credits · Graduate Programs

Parametric models of linear time-invariant systems. System and noise models. Estimation of model parameters. Structure and order selection. Model validation. Convergence and sensitivity analysis. Experiment design. MIMO systems. Subspace methods. Introduction to non-linear and/or time-varying systems. Prerequisite(s): Admission to the PhD in Electrical and Computer Engineering or the MSc in Electrical Engineering. Antirequisite(s): Credit for Electrical Engineering 631 and 684 will not be allowed.