MAS.622[J] Pattern Recognition and Analysis


Class Info

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, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research. Knowledge of probability theory and linear algebra required. Limited to 20.

This class has no prerequisites.

MAS.622[J] will not be offered this semester. It will be instructed by R. W. Picard.

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information on MIT OpenCourseWare at the Pattern Recognition and Analysis site or on the MAS.622[J] Stellar site.

MIT MAS.622[J] Pattern Recognition and Analysis Related Textbooks
MIT MAS.622[J] Pattern Recognition and Analysis On The Web

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