CS 5300 - Artificial Neural Systems

An introduction to neural net concepts, algorithms, and applications. A history of neural nets will be presented along with some discussion of models of biological neural systems. The salient features of a neural net (architecture, activation functions, weighting scheme) will be characterized. Standard algorithms will be presented including Hopfield nets, linear associative models, bidirectional associative memories, and adaptive resonance models. The student will use neural net software to experiment with standard models and to develop an application for a project.

Prerequisites/Corequisites: Prerequisite: CS 3310.

Credits: 3 hrs.

Notes: Open to Upperclass and Graduate Students. Undergraduates with junior or senior status who have met the specific course Prerequisites or have the permission of the instructor may enroll in 5000-level courses.