The aim of this course is to explore topics essential to understand mathematical and algorithmic underpinnings of modern machine learning based systems and classical Al. We will also touch upon philosophical and ethical issues in the context of AI, with a special focus on fairness in machine learning. See the lecture-wise topics for more specifics.

Specific Topics:

Introduction to Al. Flavors of Al: strong and weak, neat and scruffy, symbolic and subsymbolic, knowledge-based and data-driven. The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.

Representing Knowledge and Planning. Games, constraint satisfaction, methods for solving CSPs, Markov Decision Processes (MDPs), and reinforcement learning

Algorithmic, game-theoretic and logical foundations of multi-agent systems. Distributed optimization and problem solving, non-cooperative game theory, learning and teaching, communication, social choice, mechanism design, auctions, negotiation, coalitional game theory.

Fairness, Ethics, and Bias. Algorithmic fairness: Individual fairness, Group fairness. Applications of fairness in machine learning.

This course is aimed at CSE/AI undergraduates in their second year, as a first introduction to foundational concepts in AI.

The course is largely self-contained. Some background in elementary discrete probability and propositional logic would be useful. Knowledge of neural networks is an advantage but not a requirement.

1. Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach
2. References to specific resources (papers, etc.) will be available alongside class notes.