The Mars rover relies on sophisticated identification and estimation techniques to navigate the Martian terrain. (Image courtesy of NASA.)
Instructor(s)
Prof. Harry Asada
MIT Course Number
2.160
As Taught In
Spring 2006
Level
Graduate
Course Description
Course Features
Course Description
This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.