What you will learn
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Online Machine Learning
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Online Learnability
In many scenarios, one faces uncertain environments where a-priori the best action to play is unknown. How to obtain best possible reward/utility in such scenarios. One natural way is to first explore the environment and to identify the `best’ actions and exploit them. However, this give raise to an exploration vs exploitation dilemma, where on hand hand we need to do sufficient explorations to identify the best action so that we are confident about its optimality, and on the other hand, best actions need to exploited more number of times to obtain higher reward. In this course we will study many bandit algorithms that balance exploration and exploitation well in various random environment to accumulate good rewards over the duration of play. Bandit algorithms find applications in online advertising, recommendation systems, auctions, routing, e-commerce or in any filed online scenarios where information can be gather in an increment fashion.
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Meet Your Instructor
About Instructor
Prof. Manjesh hanawal received the M. S. degree in ECE from the Indian Institute of Science, Bangalore, India, in 2009, and the PhD degree from INRIA, Sophia Antipolis, France, and the University of Avignon, France, in 2013. After two years of postdoc at Boston University, he is now an Assistant Professor in Industrial Engineering and Operations Research at the IIT Bombay, India. His research interests include performance evaluation, machine learning and network economics. He is a recipient of Inspire Faculty Award from DST and Early Career Research Award from SERB.
Free
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Course Duration36 h 59 m 30 s
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Course LevelIntermediate
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Student Enrolled1
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LanguageEnglish
This Course Includes
- 36 h 59 m 30 s Video Lectures
- 0 Quizzes
- 0 Assignments
- 0 Downloadable Resources
- Full Lifetime Access
- Certificate of Completion