This course covers neural networks as computational models. Topics include the classification problem and the modeling of a basic neuron as a classifier, perceptrons, perceptron convergence theorem, class separability, multi-layer perceptrons, backpropagation algorithm for training, recurrent networks, associative memory, Hopfield and Kohonen networks, applications to speech, vision and control problems.