MIT OpenCourseWare

Browse materialsMIT 2.160 · Spring 2006

Identification, Estimation, and Learning

Subjects: Artificial Intelligence · Mechanical Engineering

Art: Robison, John, 1739-1805., Steam engine - Mr. Watt's double steam engine from his specification…, 1822 · Public domain

Instructor: Prof. Harry Asada
Term: Spring 2006
Level: Graduate
What's inside: 1 lecture note · 40 other files · 5 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 2.160 Identification, Estimation, and Learning, Spring 2006, Prof. Harry Asada — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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How this course was organized

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A lecture sequence of 21 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.

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Materials by section

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Lecture Notes

23 files

Assignments

11 problem sets

Exams

Other materials from this course

MIT OpenCourseWare, Prof. Harry Asada, 2.160 Identification, Estimation, and Learning, Spring 2006.

License: CC BY-NC-SA 4.0 · Edition: Spring 2006 · Open the original course on OCW ↗

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Identification, Estimation, and Learning

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Exams

Other materials from this course

Lecture 1: Introduction

Lecture 2: Recursive Least Square (RLS) Algorithms

Lecture 3: Properties of RLS

Lecture 4: Random Processes, Active Noise Cancellation

Lecture 5: Discrete Kalman Filter-1

Lecture 6: Discrete Kalman Filter-2

Lecture 7: Continuous Kalman Filter

Lecture 8: Extended Kalman Filter

Lecture 9: Prediction Modeling of Linear Systems

Lecture 10: Model Structure of Linear Time-invariant Systems

Lecture 11: Time Series Data Compression, Laguerre Series Expansion

Lecture 12: Non-linear Models, Function Approximation Theory, Radial Basis Functions

Lecture 13: Neural Networks

Lecture 14: Error Back Propagation Algorithm

Lecture 15: Perspective of System Identification, Frequency Domain Analysis

Lecture 16: Informative Data Sets and Consistency

Lecture 17: Informative Experiments: Persistent Excitation

Lecture 18: Asymptotic Distribution of Parameter Estimates

Lecture 19: Experiment Design, Pseudo Random Binary Signals (PRBS)

Lecture 20: Maximum Likelihood Estimate, Cramer-Rao Lower Bound and Best Unbiased Estimate

Lecture 21: Information Theory of System Identification: Kullback-Leibler Information Distance, Akaike’s Information Criterion