Identification, Estimation, and Learning, Spring 2006

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Author:

Asada, Harry

Subject:

Science and Technology

Institution Name:

M.I.T.

Collection:

MIT OpenCourseWare

Grade Level:

Post-secondary

Abstract:

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.

Languages:

English

Material Type:

Activities and Labs, Assessments, Full Course, Homework and Assignments, Lecture Notes, Syllabi

Media Format:

Text/HTML, Downloadable docs

Conditions of Use:

Creative Commons Attribution-Noncommercial-Share Alike 3.0