With breakthroughs in scientific computing, complex phenomena can now be reliably simulated or experimented on. However, these experiments are often expensive, and a key challenge is the design of such experiments to facilitate scalable and timely decision making. This course introduces experimental design methods for physical and computer experiments, Bayesian sampling and optimization, A/B testing and multi-armed bandits, and big data analytics. Emphasis is placed on understanding methodology and implementation for practical applications. Students should be comfortable with mathematical statistics at the level of STA 532/732 and Bayesian modeling at the level of STA 601L/602L/531.
Prerequisite: Statistical Science (532 or 732) & (531 or 601L or 602L)