CH EN 536
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Machine Learning and Dynamic Optimization
Course Description
Machine Learning and Dynamic Optimization is a graduate level course on the theory and applications of numerical solutions of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include physics-based and empirical modeling, machine learning classification and regression, nonlinear programming, estimation, and advanced control methods such as model predictive control.
When Taught
Winter
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Other Prerequisites
or equivalent to CH EN 436
Recommended
ME 575