Neural Networks

Catalog description

This course provides a working knowledge of the fundamental theory, design, and applications of Artificial Neural Networks (ANN). Topics include the major general architectures: back propagation, competitive learning, and counter propagation. Learning rules, such as Hebbian, Widrow-Hoff, generalized delta, Kohonen linear and auto associators, are presented. Specific architectures, such as the Neocognitron and Hopfield-Tank, are included. Hardware implementation is considered.