This course is an introduction to neural networks with both theoretical and practical issues being considered. Upon completion of this course, the student should understand the main neural network architectures and learning algorithms and be able to apply neural networks to real classification problems. Topics covered include single layer perceptions, multi-layer perceptions, associative memory networks, discrete hopfield networks, radial basis function networks and self-organizing networks.
