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

Massachusetts Institute of Technology. Media Laboratory.

Author(s)

Massachusetts Institute of Technology. Media Laboratory.

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Alternative title

Pattern Recognition & Analysis

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Abstract

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.

Date issued

2000-12

URI

http://hdl.handle.net/1721.1/41935

Other identifiers

MAS.622-Fall2000

local: MAS.622

local: 1.126J

local: IMSCP-MD5-4daecab298b87ed17e30c68b58fa204f

Keywords

machine and human learning, unsupervised learning and clustering, non-parametric methods, Bayesian estimation, maximum likelihood, statistical classification, decision theory, physiological analysis, computer vision, peech recognition and understanding, recognition, numerical data, MAS.622, 1.126J, 1.126, Pattern perception, Pattern recognition systems

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