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2015-2016 University Catalog
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CS 688 - Pattern RecognitionCredits: 3
Not Repeatable
Explores statistical pattern recognition and neural networks. Pattern recognition topics include Bayesian classification and decision theory, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, dimensionality reduction, feature extraction and selection, mixture models and EM, and vector quantization and clustering. Neural networks topics include feed-forward networks and back-propagation, self-organization feature maps, and radial basis functions. Emphasizes experimental design, applications, and performance evaluation.
Prerequisite(s): CS 580. Prerequisite enforced by registration system.
Hours of Lecture or Seminar per week: 3
Hours of Lab or Studio per week: 0
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