Learning in neural networks, error backpropagation, simulated annealing, content addressable memories. Data representation topics. Implementation challenges in real world scale problems. Architectures for function approximation in Reinforcement Learning. Comparison with conventional artificial intelligence: history and emerging trends. Credit will be granted for only one of SGES 592 or EECE 592. This course is not eligible for Credit/D/Fail grading.
Learning in neural networks, error backpropagation, simulated annealing, content addressable memories. Data representation topics. Implementation challenges in real world scale problems. Architectures for function approximation in Reinforcement Learning. Comparison with conventional artificial intelligence: history and emerging trends. Credit will be granted for only one of SGES 592 or EECE 592. This course is not eligible for Credit/D/Fail grading.
