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Lectures: 2 sessions / week, 1.5 hours / session
This course covers the following topics, with the aim of providing students with a broad theoretical basis for system identification, estimation, and learning:
Least squares estimation and its convergence properties, Kalman filter and extended Kalman filter, 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.
Advanced System Dynamics and Control (2.151)
There is no primary textbook for this course. Most of the course materials have been developed based on the following references:
There will be 7 homework assignments and 2 two-hour exams. Each student is also expected to complete 1 project.