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Course Information

This course will explore foundational topics and techniques in Artificial Intelligence, as well as sample some applications and areas of active research. The first part of the course will cover classical topics such as search and constraint satisfaction. The second part of the course will delve into probabilistic graphical models and inference methods, necessary skills for handling uncertainty in AI systems. The third part will touch on the basics of machine learning and neural networks,  with a more focused exploration of Reinforcement Learning. The list of tentative topics is:

• Search methods

• Constraint satisfaction

• Probabilistic graphical models

• Probabilistic inference

• Decision theory

• Neural networks & ML basics

• Reinforcement Learning

• Ethics and biases in AI

The course will be at an advanced undergraduate/beginner graduate level. The course will presume a functional understanding of probability, statistics, linear algebra, and specific CS topics such as Dijkstra's algorithm. The assignments and project will have substantial programming components.