Identification, Estimation, and Learning, Spring 2006
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| Type: | Course Related Materials |
| Grade Level: | Post-secondary |
Author: Asada, Harry
Subject: Science and Technology
Institution Name:
M.I.T.
Collection Name: MIT OpenCourseWare
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.
Details
Course Type: Full Course
Material Types: Syllabi, Homework and Assignments, Assessments, Activities and Labs, Lecture Notes
Media Formats: Text/HTML, Downloadable docs
Language: English
Additional Information
Geographic
Regional Relevance: All
