A graduate-level course on machine learning techniques and applications with emphasis on their application to systems engineering. Topics include: Bayesian learning, evolutionary algorithms, instance-based learning, reinforcement learning, and neural networks. Students are required to have sufficient computational background to complete several substantive programming assignments.
Prerequisite: A course covering statistical techniques such as regression.
|5:00 - 6:15 PM||•||5:00 - 6:15 PM||•||•||•|
Dr. William Guilford • Ph: 434-243-2740