Course info

Fundamentals of Neural Networks; Back propagation and related training algorithms; Hebbian learning; Cohonen-Grossberg learning; The BAM and the Hopfield Memory; Simulated Annealing; Different types of Neural Networks: Counter propagation, Probabilistic, Radial Basis Function, Generalized Regression, etc; Adaptive Resonance Theory; Dynamic Systems and neural Control; The Boltzmann Machine; Self-organizing Maps; Spatiotemporal Pattern Classification, The Neocognition; Practical Aspects of Neural Networks.