Instructor's consent and Must: Solid understanding of Machine Learning (e.g, CS771 or equivalent course), theoretical and practical knowledge of Deep Learning, Proficiency in Linear Algebra, Probability and Statistics, Proficiency in Python Programming.
Probabilistic Machine Learning (CS772), Topics in Probabilistic Modeling and Inference (CS775), Deep Learning for Computer Vision (CS776)
Senior UG and PG (6xx level)
In this course, we will explore how an agent (via interactions with the environment) can learn by trial and error. This is quite different from supervised machine learning and comes close to how humans learn by interactions. Reinforcement Learning (RL) deals with problems that require sequential decision making. This course will explore the foundations of reinforcement learning. We will study different algorithms for RL and later in the course, we will explore how functional approximation in RL algorithms could be done using neural networks giving rise to deep reinforcement learning.
This course will follow the book given below but it also gleans information from a variety of sources like research papers, other courses, etc. Relevant references would be suggested in the lectures.