Sample Efficient Optimization

This course presents methods that combine domain knowledge from management, science or engineering within classical ML and OR techniques to yield practice-relevant optimization tools. Topics include Gaussian process modeling, Bayesian optimization, dimensionality reduction, variance reduction, continuous and/or discrete simulation-based optimization, physics-informed optimization, surrogate modeling. Concepts are illustrated using practical applications. The course presents established and emerging research through lectures and paper discussions. It includes guest research lecturers from major technology companies and / or top academic institutions worldwide.