University of Arizona 3 credits Systems & Industrial Engr
We consider optimization problems whose objective functions are unknown and hence have to be learned from data. Such problems are pervasive in science and industry, e.g., when - designing prototypes in engineering, - automated tuning of machine learning algorithms, e.g., in deep learning, - optimizing control policies in robotics, - developing pharmaceutical drugs, and many more. Bayesian optimization methods are popular in the machine learning community due to their high sample-efficiency and have become a key technique in the area of \"automatic machine learning\". We introduce a general framework in which to understand and formulate such optimal learning problems, and provide a survey of problems, methods, and theoretical results.
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