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2015-2016 University Catalog
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CS 775 - Advanced Pattern RecognitionCredits: 3
Not Repeatable
Covers statistical pattern recognition, neural network, and statistical learning theory approaches. Topics include decision theory and Bayes’ theorem, density (parametric and nonparametric) estimation, linear and nonlinear discriminant analysis, SVM and kernel methods, SRM and model selection, performance evaluation, mixture of experts (AdaBoost), dimensionality reduction, feature selection and extraction, and clustering. Emphasizes experimental design, applications, and performance evaluation.
Prerequisite(s): CS 688. 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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