This course provides an introduction to concepts in neural networks and connectionist models. Topics include parallel distributed processing, learning algorithms, and applications. Specific networks discussed include Hopfield networks, bidirectional associative memories, perceptrons, feedforward networks with back propagation, and competitive learning networks, including self-organizing and Grossberg networks. Software for some networks is provided. (This course is the same as 605.447 Neural Networks.)
Course prerequisites: 
Multivariate calculus and linear algebra.
Course instructor: 
Fleischer

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Course all programs: 
Applied and Computational Mathematics
Data Science