Artificial Intelligence: Foundations and Applications

Course objectives

The primary objective of this course is to indroduce the gamut of real-life problems where AI techniques can be successfully applied and detailing the necessary foundations to enable solving those practical problems. Hence, the contents of the course will revolve around several practical case-studies and provide foundational understanding to apply AI methods in solving them effectively. This course shall also provide necessary theoretical insights spanned across four layers – knowledge representation and logic; search and reasoning frameworks; ramifications of AI techniques under various scenarios/constraints; and observations/updations of methods from learning. These foundational and theoretical aspects will be enabled by suitable tools and appropriate communication interface designs, thereby bringing a holistic view of problem solving through applications of AI techniques.

Course prerequisite

Programming and Data Structures

L-T-P & Credit

3-1-0 (4 credit)

Class hours

Monday (1500-1700)
Tuesday (1400-1600)

Venue

MS Teams

Syllabus

Evolution and Domains of applications, Knowledge representation methods – state-space, knowledge graph, propositional logic, Search – heuristic, A*, CSP, DFBB, Reasoning, Planning, Learning, Communications, Real world consideration – time / memory bound, uncertainty, distributed, multi-agent, etc., Case studies – transportation, robotics, weather forecast, game design, VLSI system design, etc.

Instructors

Partha Pratim Chakrabarti
Arijit Mondal

Teaching assistants

Omprakash Chakraborty
Dipankar Mandal
Atif Hassan
Anupam Borthakur

Course schedule