We present a case study on applying Bayesian Optimization to a complex real-world
system; our challenge was to optimize chocolate chip cookies. The process was a
mixed-initiative system where both human chefs, human raters, and a machine
optimizer participated in 144 experiments. This process resulted in highly rated
cookies that deviated from expectations in some surprising ways -- much less sugar
in California, and cayenne in Pittsburgh. Our experience highlights the importance
of incorporating domain expertise and the value of transfer learning approaches.