Identification, Estimation, and Learning OpenCourseWare: A Free MIT Grad-Level Course on System Identification and Estimation

Published Jan 26, 2009

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Students taking part in MIT's 'Identification, Estimation and Learning' OpenCourseWare project will learn about the theoretical basis for system identification, estimation and learning. Specific topics to be covered in the course include Kalman filters, neural nets, radial basis functions, wavelets and informative data sets. A background in Mechanical Engineering is required for the successful completion of the course.

Identification, Estimation, and Learning: Course Specifics

Degree Level Free Audio Video Downloads
Graduate Yes No No Yes

Lecture Notes Study Materials Tests/Quizzes
Yes Yes Yes

Identification, Estimation, and Learning: Course Description

'Identification, Estimation, and Learning,' offered by Professor Harry Asada, is designed for students interested in learning about the theoretical basis for system identification, estimation and learning. Specific topics to be discussed include radial basis functions, central limit theorems, experiment design, model validation, asymptotic variance and persistent excitation. The course is split into three sections, which are estimation, representation and learning and system identification.

Lecture notes, homework assignments and two exams are available for download for free online. If you are interested in taking this free OpenCourseWare, visit the estimation and learning course page.

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