Course abstract
Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI. The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research.
Course Instructor
Dr. Balaraman Ravindran
Prof. Balaraman Ravindran completed his Ph.D. at the Department of Computer Science, University of Massachusetts, Amherst. He worked with Prof. Andrew G. Barto on an algebraic framework for abstraction in Reinforcement Learning. Dr. Ravindrans current research interests spans the broader area of machine learning, ranging from Spatiotemporal Abstractions in Reinforcement Learning to social network analysis and Data/Text MiningMore info
Teaching Assistant(s)
Course Duration : Jul-Oct 2016
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Syllabus
Enrollment : 23-May-2016 to 18-Jul-2016
Exam registration : 25-Jul-2016 to 20-Sep-2016
Exam Date : 16-Oct-2016, 23-Oct-2016
Enrolled
1230
Registered
6
Certificate Eligible
3
Certified Category Count
Gold
0
Elite
0
Successfully completed
3
Participation
1
Success
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Category : Successfully Completed
Elite
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Category : Elite
Gold
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Category : Gold
Legend
>=90 - Elite + Gold
60-89 - Elite
40-59 - Successfully Completed
<40 - No Certificate
Final Score Calculation Logic
- Assignment Score = Average of best 8 out of 12 assignments.
- FINAL SCORE (Score on Certificate) = 75% of Exam Score + 25% of Assignment Score.
- NOTE:Assignmnet 0 has not been been considered for calculation of assignment score which is been instructed by Professor
Reinforcement Learning - Toppers list
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