2 160 - Identification, Estimation, and Learning

Institution:

Massachusetts Institute of Technology

Subject:

Description:

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.

Credits:

3.00

Credit Hours:

Prerequisites:

Prereq: 2.151

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

web.mit.edu/student/

Phone Number:

(617) 253-1000

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Four-one-four plan

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