MAS.622J Pattern Recognition and Analysis (MIT)

MIT OpenCourseWare: New Courses in Media Arts and Sciences 2013-03-28


This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the 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. We also cover 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 are also talked about in the class. Email this Article Add to Facebook Add to Twitter Add to digg Add to Google


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#edutech ยป MIT OpenCourseWare: New Courses in Media Arts and Sciences


genetic algorithms roc curves pattern recognition feature detection classification probability theory pattern analysis conditional probability bayes rule random vectors decision theory likelihood ratio test fisher discriminant template-based recognition feature extraction eigenvector and multilinear analysis linear discriminant perceptron learning optimization by gradient descent support vecotr machines k-nearest-neighbor classification parzen estimation unsupervised learning clustering vector quantization k-means expectation-maximization hidden markov models viterbi algorithm baum-welch algorithm linear dynamical systems kalman filtering bayesian networks decision trees reinforcement learning


Faculty and Staff, Media Lab

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Date tagged:

03/28/2013, 16:16

Date published:

10/04/2007, 17:20