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

Past Offerings

Course Metadata

Item	Details
Course Title	Introduction to Artificial Intelligence
Course Code	DS2020
Course Credits	3-0-2-4
Course Category	PMC
Proposing Faculty	Narayanan C Krishnan
Approved on	Senate 20 of IIT Palakkad
Course prerequisites	Introduction to Programming,Discrete Mathematics
Course status	NEW