Artificial Neural Networks

Undergraduate

Practical problem solving using artificial neural networks. Supervised learning, single- and multilayer feed-forward networks and backpropagation and refinements; recurrent neural networks; Hopfield networks and Boltzmann machines. Unsupervised learning, competitive learning, Kohonen map and self-organizing feature maps. Course Format: Lectures, lab, 5 hours per week. Prerequisite(s): COSC 3P71 (minimum 60 percent); MATH 1P12; MATH 1P02 or MATH 1P06 Course Notes: This course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.