MAS.622 Pattern Recognition and Analysis
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 will be offered this semester (Fall 2019). It is instructed by R. W. Picard.
Lecture occurs 10:30 AM to 12:00 PM on Mondays and Wednesdays in E14-633.
This class counts for a total of 12 credits.
In the Fall 2010 Subject Evaluations, MAS.622 was rated 4.6 out of 7.0. You can find more information at the DSpace@MIT: MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000 site.
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