2.160 Identification, Estimation, and Learning

Spring 2006

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.)

Course Highlights

This course features extensive lecture notes and many assignments.

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.

Technical Requirements

Special software is required to use some of the files in this course: .zip. The .txt files in the assignments section are used for MATLAB®.

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Staff

Instructor:
Prof. Harry Asada

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Level

Graduate