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IE 617: Online Machine Learning and Bandit Algorithms

Prerequisite: IE 621 or IE 611 or EE 325 or EE 601 or CS 723 or IE 605 or DS 203 or SC 629 or EE 734



Brief introduction to batch learning methods and algorithms, Introduction to online learning methods: adversarial and stochastic settings, Online learning with expert feedback, bandit feedback, partial monitoring: algorithms and their analysis, Pure exploration algorithms, Introduction to reinforcement learning and connection to bandits



T. Lattimore and C. Szepesvari, ``Bandit Algorithms,302222302222 Cambridge Press, 20192) S. Shalev-Shwartz and S. Ben-David, ``Understanding Machine Learning: From Theory to Algorithms,``Cambridge University Press, 20143) S. Shalev-Shwartz, Foundation and Trends in machine learning, ``Online Learning and Online Convex Optimization,``NOW publisher, 24) S. Bubeck and N. Cesa-Bianchi, ``Regret Analysis of Stochastic and Nonstochastic Multi-armedBandit Problems,`` Foundation and Trends in machine learning, NOW publisher, 25) N. Cesa-Bianchi and G. Lugosi, ``Prediction, Learning, and Games, Cambridge University Press, 2006