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

Title:	MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000
Author:	Massachusetts Institute of Technology. Media Laboratory.
Issue Date:	2000-12
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.
URI:	http://hdl.handle.net/1721.1/41935
Other Identifiers:	MAS.622-Fall2000
Other Identifiers:	MAS.622 1.126J 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, 090101, Communications Studies/Speech Communication and Rhetoric

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