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Neural Networks (3 credits)

An introduction to artificial neural networks. Topics include a survey of natural neural network models, perceptrons and their limitations, multi-layer networks and back propagation, Hebbian learning, unsupervised competitive learning, relations to automata and computability theory, adaptive resonance theory, applications of connectionist models of computing to various domains, including pattern recognition, databases, etc.


An understanding of multi-variable calculus and associated linear algebra including gradient methods is required, some exposure to statistics and probability is advised; permission of the instructor.

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