2.160 | Spring 2006 | Graduate

Identification, Estimation, and Learning


Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Course Description

This course covers the following topics, with the aim of providing students with 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.


Advanced System Dynamics and Control (2.151)


There is no primary textbook for this course. Most of the course materials have been developed based on the following references:

Ljung, Lennart. System Identification: Theory for the User. 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999. ISBN: 9780136566953.

Goodwin, Graham, and Kwai Sang Sin. Adaptive Filtering, Prediction, and Control. Englewood Cliffs, NJ: Prentice-Hall, 1984. ISBN: 9780130040695.

Burnham, Kenneth, and David Anderson. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York, NY: Springer, 2003. ISBN: 9780387953649.

Brown, Robert, and Patrick Hwang. Introduction to Random Signals and Applied Kalman Filtering. 3rd ed. New York, NY: Wiley, 1996. ISBN: 9780471128397.

Grading Policy

There will be 7 homework assignments and 2 two-hour exams. Each student is also expected to complete 1 project.

Activities Percentages
First Exam 30%
Second Exam 30%
Homework Assignments 20%
Term Project 20%