1.126[J] 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.
1.126[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.
In the Fall 2010 Subject Evaluations, 1.126[J] 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.
© Copyright 2015 Yasyf Mohamedali