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Pattern Recognition and Analysis, Fall 2006
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Fundamentals of characterizing and recognizing patterns and features of interest in numerical ...

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.

Subject:
Statistics and Probability
Science and Technology
Material Type:
Activities and Labs
Full Course
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Provider:
M.I.T.
Provider Set:
M.I.T. OpenCourseWare
Author:
Picard, Rosalind