Prerequisites: graduate standing; or consent of instructor

Description: Introduces methods and techniques for data-driven modeling and control of dynamical systems. Topics include prediction-error modeling, subspace methods, non-parametric modeling, experiment design, model validation, model- predictive control, model-free control, and hyperdimensional methods for modeling and control of nonlinear systems. May be Taken Satisfactory (S) or No Credit (NC) by students advanced to candidacy for the Ph.D.