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EECE 592 Architecture for Learning Systems
Learning in neural networks; error backpropagation, simulated annealing, content addressable memories. Data representation topics. Reinforcement learning (RL). Implementation challenges in real world scale problems. Architectures for function approximation in RL. Comparison with conventional AI; history and emerging trends.
- This course is restricted to students in one of these faculties: GRAD
|Status||Section||Activity||Term||Interval||Days||Start Time||End Time||Comments|
|Full||EECE 592 101||Lecture||1||Wed||18:00||21:00|