2.160 Identification, Estimation, and Learning


Class Info

Provides 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.

This class has 2.151 as a prerequisite.

2.160 will not be offered this semester. It will be available in the Spring semester, and will be instructed by H. Asada and J.-J. E. Slotine.

This class counts for a total of 12 credits. This is a graduate-level class.

In the Spring 2015 Subject Evaluations, 2.160 was rated 5.3 out of 7.0. You can find more information on MIT OpenCourseWare at the Identification, Estimation, and Learning site or on the 2.160 Stellar site.

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Identification, Estimation, and Learning
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