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An introduction to neural network models and their applications. Discussion of organization and learning in neural network models including: Perceptions, Adalines, back propagation networks, recurrent networks, adaptive resonance theory and the neocognitron. Implementations in general and special purpose hardware, analog and digital. Application in various areas with comparisons to non-neural approaches. Decision systems, nonlinear control, speech processing and vision.
Prerequisites: CS540. Some familiarity with matrix notation and partial derivatives is recommended