Course website
Course schedule, lecture slides, additional suggested readings, and other course material will be constantly posted here.
Course description
This course provides a broad introduction to the foundational ideas and techniques of Artificial Intelligence, together with the engineering challenges involved in building modern AI systems. Topics include search, constraint satisfaction, logic, probabilistic reasoning, machine learning, sequential decision making, reinforcement learning, and the foundations of contemporary systems such as large language models and AI agents. By the end of the course, students will be prepared to analyze, build, and critically evaluate AI systems and to pursue further study of modern AI methods and applications.
Prerequisites: Graduate standing, and the equivalent of EECS 281 and its prerequisites. We assume programming experience and familiarity with algorithmic concepts such as graph search and computational complexity, along with comfort in probability, linear algebra, multivariate calculus, and general mathematical proof techniques. We will employ rigorous mathematical reasoning when appropriate.
Textbook:
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Fourth Edition, 2020.
Additional readings will be suggested during the term.