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Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: Approved for letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Prerequisites: background in basic probability theory. linear algebra, and algorithm design and analysis. This course covers several foundational topics in online learning and sequential decision making under uncertainty, a subject on the intersection of algorithms, machine learning, and operations research. Such problems have wide applications in online advertising, recommendation systems, crowdsourcing, revenue management, etc. In this course, we will study the problems that usually feature the tension between how to collect data and utilize the data to make optimal sequential decisions (a.k.a. the exploration and exploitation dilemma). We cover both fundamental results and research frontiers. We focus on algorithmic results, and introduce lower bounds as well.