Course Information

CS 748: Advances in Intelligent and Learning Agents

Advanced topics and research projects building on topics covered in ``Foundations of Intelligent and Learning Agents``. Specific topics covered could include, for example, the following. (1) Contextual bandits (2) Partially Observable Markov Decision Problems (3) Function approximation for reinforcement learning (4) Sample complexity of reinforcement learning (5) Monte Carlo tree search (6) Evolutionary algorithms

Primarily research papers from the last two decades, and advanced topics from the following texts.(1) Artificial Intelligence: A Modern Approach, Stuart J. Russell and Peter Norvig, 3rd edition, Prentice-Hall, 2009.(2) Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, MIT Press, 1998.(3) Dynamic Programming and Optimal Control, Volume II, Dimitri P. Bertsekas, 4th edition, Athena Scientific, 2.(4) Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Sebastien Bubeck and Nicolo Cesa-Bianchi, Foundations and Trends in Machine Learning, Volume 5, Number 1, 2.
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Duration : Full Semester Total Credit : 6
Type : Theory
Autumn Semester 2019-20

Status : Not Offered Instructor : ---
Spring Semester 2019-20

Status : Offered Instructor : Prof. Shivaram Kalyanakrishnan

Last Modified Date: 15-Jul-2013


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