| Introduction to the theory, architectures, and application of Artificial Neural Systems. Topics include fundamental models of artificial neural systems, learning rules, supervised, unsupervised and reinforcement learning in single and multi-layer neural networks, radialbasis function networks, principal component analysis, self-organizing maps, adaptive resonance theory, stochastic machines, learning capacity and generalization. Prerequisites: CS 2303 and 3 terms of calculus and statistics, or permission of instructor. |
