Abstract: The information revolution is spawning systems that require frequent decisions and generate high volumes of data. Fueling the design of algorithms used in such systems is a vibrant research area at the intersection of sequential decision-making and machine learning that addresses how to balance between exploration and exploitation while generalizing from experience to make increasingly effective decisions. I will discuss approaches to the design of such algorithms, including upper-confidence bounds, Thompson sampling, and information-directed sampling, and trade-offs among them.