Open Course Space

Identification, Estimation, and Learning

Massachusetts Institute of TechnologyUnited States2.160
Free, no sign-up neededSpring 2006English
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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…

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