Undergraduate Prerequisites: (CDSDS122 or CASMA581) and (CDSDS320 or equivalent.) - Topic for Spring 2025: Introduction to Sequential Decision Making This course introduces the study, design and analysis of algorithms for sequential decision making with a particular focus on bandit algorithms and other topics in statistical learning theory. Designed for upper undergraduate and graduate students, the course covers foundational concepts and cutting-edge research in multi-armed bandits, linear bandits, and contextual bandits. Students will gain an understanding of fundamental algorithmic principles in sequential decision making such as optimism, multiplicative weights as well as bandit algorithms such as UCB, EXP3, OFUL. Additionally, the class will cover bandit problems in the general function approximation regime via the study of algorithms such as SquareCB and statistical dimensions for function approximation, including the eluder dimension, dissimilarity dimension, and decision estimation coefficient. Finally, the course will also explore miscellaneous yet essential topics such as online model selection, and offline estimation. Through a combination of theoretical insights and practical applications, students will gain a comprehensive understanding of how to design, analyze, and implement algorithms for sequential decision-making tasks.

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