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Material Detail

MAS.622J Pattern Recognition and Analysis | Media Arts and Sciences

MAS.622J Pattern Recognition and Analysis | Media Arts and Sciences

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.
Keywords:
linear discriminant, random vectors, Baum-Welch algorithm, pattern analysis, K-nearest-neighbor classification, K-means, Probability and Statistics, genetic algorithms, ROC curves, unsupervised learning, Signal Processing, classification, viterbi algorithm, feature detection, clustering, decision trees, Expectation-Maximization, conditional probability, support vecotr machines, Bayesian networks, vector quantization, optimization by gradient descent, template-based recognition, decision theory, Cognitive Science, eigenvector and multilinear analysis, Hidden markov models, likelihood ratio test, fisher discriminant, linear dynamical systems, reinforcement learning, feature extraction, bayes rule, Kalman filtering, perceptron learning, probability theory, pattern recognition, parzen estimation
Disciplines:
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More about this material

Material Type:
Online Course
Date Added to MERLOT:
May 12, 2016
Date Modified in MERLOT:
May 12, 2016
Author:
Unknown (Know the author?)
Submitter:
Barbra Bied Sperling
Hispanic Serving Institution
Primary Audience:
Graduate School
Technical Format:
Website
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States

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