Degree Level Course

Sequential Decision Making

The course introduces the basic concepts and algorithms related to sequential decision making. Based on the type of feedback received by a system, two types of sequential learning paradigms will be introduced: online learning (full information) and multi-armed bandits (partial information). Algorithms and applications relevant to each paradigm will be discussed.

by Arun Rajkumar

Course ID: BSDA6004

Course Credits: 4

Course Type: Elective

Pre-requisites: None

Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page. For details of standard course structure and assessments, visit Academics page.

Module 1 Introduction to Online Learning, Halving algorithm, Online Machine Learning; Perceptron and Winnow, Intro to Regret; Online learning with expert advice -Hedge algorithm Online linear optimization, Online convex optimization; Online learning summary
Module 2 Introduction to Multi armed Bandits Adversarial Bandits - EXP3 algorithm, Contextual MAB - EXP4 algorithm, Stochastic MAB, Epsilon Greedy, Explore then commit, Stochastic MAB, UCB, Thompson Sampling, Stochastic MAB - Linear Bandits LinUCB algorithm; MAB summary
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Prescribed Books

The following are the suggested books for the course:

Bubeck S. Introduction to online optimization. Lecture notes. 2011 Dec 14;2:1-86.

Bubeck, S. and Cesa-Bianchi, N., 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, 5(1), pp.1-122.

About the Instructors

Arun Rajkumar
Assistant Professor, Department of Data Science and AI, IIT Madras

I am currently an Assistant Professor at the Data Science and AI department of IIT Madras. Prior to joining IIT Madras, I was a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore for three years. I earned my Ph.D from the Indian Institute of Science where I worked on 'Ranking from Pairwise Comparisons'. My research interests are in the areas of Machine learning, statistical learning theory with applications to education and healthcare.

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Other courses by the same instructor: BSCS2004 - Machine Learning Foundations , BSCS2007 - Machine Learning Techniques and BSDA5007 - Reinforcement Learning

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