Graduate Catalog 2016-2017
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CSC 732 Pattern Recognition and Neural Networks
3 hours; 3 credits
Topics of the course will initially survey pattern recognition systems and components; decision theories and classification: discriminant functions: classical supervised and unsupervised learning methods, such as backpropagation, radial basis functions: clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; Kohonen networks; Boltzman machines, principal components, and examples of applications. Modern concepts in learning will be introduced: nonparametric learning, reinforcement learning, mixtures models, belief networks, minimum description length, maximum likelihood, entropy methods, independent component analysis.
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