3.0

credits

Average Course Rating

Humans, even young children, can learn, model, and reason about the world and other people in a fast, robust, and data efficient way. This course will discuss the principles of human cognition, how we can use machine learning and AI models to computationally capture these principles, and how these principles can help us build better AI. Topics will include (but are not limited to) Bayesian concept learning, probabilistic programming, intuitive physics, decision-making, Theory of Mind, pragmatics, and value alignment. Required Course Background: Prob/Stat & Linear Algebra & Computing; prior course in ML/AI strongly recommended. Students may receive credit for only one of 601.473/601.673.