Reinforcement Learning
Course number: COMP 442, COMP 552
Taught during: Fall 2022, Fall 2023
Course description: This course introduces students to reinforcement learning (RL), a general and impactful machine learning paradigm for solving sequential decision-making problems and designing autonomous agents. The course covers both classical and recent algorithms for reinforcement learning and imitation learning. Through the assignments and final project, students get hands-on experience in applying reinforcement learning algorithms to solve problems inspired by real-world applications. The course concludes with an overview of open problems and ongoing research in reinforcement learning.
Interactive Machine Learning
Course number: COMP 641
Taught during: Fall 2021, Spring 2023, Spring 2024
Course description: Many applications of machine learning involve humans in the loop (e.g., the programmer implementing the algorithm, the domain expert specifying the features/labels, or the end user making decisions using the learned model). This course is a discussion-based seminar focusing on the design, analysis, and evaluation of machine learning techniques with explicit emphasis on the human(s) in the loop. Topics include reinforcement learning with human teachers, active learning, interpretability, learning beyond labels, and human-in-the-loop Bayesian inference.
Computational Human-Robot Interaction
Course number: COMP 565
Past offerings: Spring 2021
Course description: The course provides an introduction to the budding field of human-robot interaction (HRI), with emphasis on its computational aspects. The course covers models and algorithms for learning robot policies from human expertise, modeling human behavior using observational data, and enhancing human-robot coordination. Through problems grounded in HRI, students also learn about general AI techniques for imitation learning (e.g., inverse reinforcement learning) and sequential decision-making under uncertainty (namely, partially observable Markov decision processes or POMDPs).