Course Code: DA221M	Course Name: Elements of Artificial Intelligence	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Introduction to AI and Intelligent Agents; Problem solving by Searching: Uninformed and informed strategies; Logical Agents: Propositional and first order logic, inference; Knowledge representation and Automated Planning; Uncertain Knowledge and Reasoning: Quantifying uncertainty, probabilistic reasoning. Introduction to Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning; Markov Process: Discrete-time Markov chain, Stationary Distribution; Markov Decision Process: Dynamic Programming, Finite Horizon MDP, Infinite Horizon MDP; Value Iteration Algorithm, Policy Iteration Algorithm; Multi-armed Bandit, Application & Case Studies; Q-learning; TD learning; Policy Gradient.
Textbooks: S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Ed., Pearson, 2020. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd Ed., MIT Press, 2018.
References: E. Rich, K. Knight, and S. B. Nai, Artificial Intelligence, McGraw Hill, 3rd Ed., 2017. D. Khemani, A First Course in Artificial Intelligence, 1st Ed., McGraw-Hill Education, 2017.