Machine Learning via Sequential Interaction (MATH-0169)

Spring 2026 · MATH 0169 · Tufts University · 3 credits · Undergraduate

Introduction of machine learning as a sequential and interactive game technically referred to as 'online learning'. In online learning, the learner aims to minimize the regret: loss incurred for not playing the best strategy (in hindsight) averaged over time. This framework generalizes the traditional statistical learning set-up in a non-trivial manner to cases where the examples are not independent, are supplied only sequentially, and possibly in an adversarial manner. Topics include: general set-up of sequential learning with examples, online convex and online linear optimization, online-to-batch conversion, multi-armed bandits, contextual bandits, practical algorithms and their analysis with provable guarantees. Connections with and extensions to reinforcement learning will be highlighted. Prior programming experience is recommended.

Course codes: MATH-0169, MATH 0169, MATH0169, MATH-169, MATH 169, MATH169

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