Prerequisite (if any): Introduction to Programming, Discrete Mathematics

Course Content

  1. Introduction to AI ­ Rational Agents ­ [2 lectures]

  2. Search­(BFS, UCS, A* Search, Heuristics, Local Search) ­ [4 lectures, 6 lab hours]

  3. Adversarial Search [3 lectures, 6 lab hours]

  4. Knowledge Representation and logical inference (propositional logic, resolution, predicate logic, ontologies)[9 lectures]

  5. Planning [4 lectures]

  6. Probabilistic reasoning (probabilistic graphical model, exact and approximate inference)[5 lectures, 4 lab hours]

  7. Markov Decision Processes (introduction to mdp) [6 lectures, 6 lab hours]

  8. Decision making under uncertainty (introduction to PoMDPS, game theory, mechanism design, [3 lectures]

  9. Reinforcement learning (introduction to rl) [6 lectures, 6 lab hours]

Learning Outcomes

  • Learn the fundamentals of field artificial intelligence. The students will be familiar with a broad range of topics in AI, ready to undertake specialized courses.

  • Gain hands-on programming experience through the implementation of the AI techniques for various synthetic and real world applications.

Textbooks

Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Pearson, 2020. ISBN 978­0134610993

Reference Books

Artificial intelligence: Kevin Knight, Elaine Rich, Shivashankar Nair, McGraw Hill, 3rd Edition 2017 ISBN 978-0070087705