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CSCI152 HM - Neural Networks

Credit(s): 3

Instructor(s): Keller

Offered: Fall

Description: Modeling, simulation, and analysis of artificial neural networks and their relation to biological networks. Design and optimization of discrete and continuous neural networks. Back propagation and other gradient descent methods. Hopfield and Boltzmann networks. Unsupervised learning. Self-organizing feature maps. Applications chosen from function approximation, signal processing, control, computer graphics, pattern recognition, time-series analysis. Relationship to fuzzy logic, genetic algorithms, and artificial life.

Prerequisite(s): (CSCI060 HM  or CSCI042 HM ) and MATH065 HM