Neurocomputing

Course content

Learning and generalization. The basic perceptron and linear separability. Multilayer perceptrons and the backpropagation algorithm. The Hopfield model and its dynamics. Bidirectional associative memory. Recurrent networks. Unsupervised learning and self-organizing maps. The counter- propagation network. Boltzman machine and simulated annealing. Recent advances.