Data-Driven Optimization

4 credits

Foundational optimization techniques used across modern engineering, including machine learning and control theory. Covers fundamentals as well as deep-dives into relevant topics, such as: convex and nonconvex optimization, constrained optimization, high-dimensional and stochastic techniques for big data, algorithms and computational techniques, the calculus of variations, and case studies. Recommended: proficiency with writing code in Python. Offered: W.