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MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000

Patterns of light.
Patterns of light.  (Photo © openphoto.net.)

Highlights of this Course

This course contains problem sets related to recognizing numerical patterns, as well as example solutions in the related resources section. The tools section contains a useful MATLAB tutorial to get you started. Raw datasets and examples of their use are in the projects section.

Course Description

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

 

Staff

Media Lab Faculty

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Level

Graduate

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