Interactive Machine Learning and Reinforcement Learning

Catalog description

The course introduces fundamental concepts, principles, and essential components in interactive machine learning (IML) and reinforcement learning (RL). Students gain individual experience creating and evaluating one or more components of an RL solution to a stated problem. A group-based class project plays a central role in the course, where teams formulate, implement, evaluate, and deploy an IML system from backend to frontend. Students engage in critical thinking about IML/RL methodologies, and the challenges and opportunities that arise when AI systems learn efficiently from other agents such as humans or AI systems. The course also introduces several applications such as recommendation systems, robotics, and games.