CMPS 435B Introduction to Neural Networks
Neural networks represent an emerging technology and are becoming increasingly versatile. They can solve difficult, solvable nonlinear problems using traditional methods. Inherently parallel design and the ability to interact with the environment make neural networks ideal for large applications. This course will consider the design and implementation of neural networks. Topics include neural networks as problem-solving tools; neural networks as self-organizing systems; single or multi-layered perceptions; associative memory networks; techniques in neural learning, back-propagation, and supervised and unsupervised learning. Issues related to neuro-computing hardware and neuro-VLSI implementation will be discussed.
Prerequisite
Departmental permission only