Identification, Estimation, and Learning

A photograph of the Mars rover.

The Mars rover relies on sophisticated identification and estimation techniques to navigate the Martian terrain. (Image courtesy of NASA.)


MIT Course Number


As Taught In

Spring 2006



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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.

Asada, Harry. 2.160 Identification, Estimation, and Learning, Spring 2006. (MIT OpenCourseWare: Massachusetts Institute of Technology), (Accessed). License: Creative Commons BY-NC-SA

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