# Identification, Estimation, and Learning

The Mars rover relies on sophisticated identification and estimation techniques to navigate the Martian terrain. (Image courtesy of NASA.)

### Instructor(s)

Prof. Harry Asada

### MIT Course Number

2.160

### As Taught In

Spring 2006

### Level

Graduate

###

Some Description
Instructor(s) | Prof.
As Taught In | Spring 2002
Course Number | 2.24
Level | Undergraduate/Graduate
Features | Lecture Notes, Student Work

## Course Description

## Course Features

- Lecture notes
- Assignments: problem sets (no solutions)
- Exams (no solutions)

## Course 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.

## Related Content

## Course Collections

See related courses in the following collections:

### Find Courses by Topic

- Mechanical Engineering > Dynamics and Control
- Computer Science > Artificial Intelligence
- Electrical Engineering > Signal Processing
- Systems Engineering > Numerical Simulation
- Computer Science > Algorithms and Data Structures

Harry Asada. 2.160 Identification, Estimation, and Learning. Spring 2006. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.

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