This course introduces the available methods in machine learning, which methods are better in what situations, objective functions and errors, and how to tune parameters.

Theory and implementation of practical methods to find good or optimal solutions to optimization problems too large or complex to solve in a straightforward way.

Topics include quality system requirements, designed experiments, process capability analysis, measurement capability, statistical process control, and acceptance sampling plans. Credit will not be awarded for both ISYE 3039 and ISYE 6382.

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Independent study in topics at the intersection of extended reality (XR), artificial intelligence/machine learning, and human–robot interaction. Emphasis on the design, development, and evaluation of human-centered systems for applications such as immersive training, human–AI teaming, and human augmentation

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Topics include neural networks, support vector machines, classification trees, boosting and discriminant analyses. Intended for Ph.D. students and those seeking the M.S. in Statistics.