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Identification, Estimation, and Learning

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, un...

Start Date: Feb 01, 2006 Topics: Design
Cost: Free

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Overview

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.

Details

  • Dates: Feb 01, 2006 to May 25, 2006
  • Days of the Week: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
  • Level of Difficulty: Beginner
  • Size: Massive Open Online Course
  • Instructor: Prof. Harry Asada
  • Cost: Free
  • Institution: MIT OCW
  • Topics: Design

Provider Overview

About MIT OCW: MIT OpenCourseWare (OCW) is a web-based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity.

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