Fall 2023 | Prof. Benjamin Eysenbach | Mon/Wed 1:30pm -- 2:50pm in Friend Center 006
- Where is class? Friend Center 006.
- The class is full? Please fill out the waitlist form. No need to email me. Attend class and I’ll let you know the plan for enrollment (I’m working to get a TA that will help us increase the cap a bit.).
- First class: Wed Sept 6.
- Is it OK that I haven’t taken COS 324, but have taken another ML course? Yes, as long as you’re familiar with the RL and mathematics for ML portions of 324.
- Office hours will be immediately followly Wednesday’s classes: Wed 3:00pm – 4:00pm in CS 416.
- Other questions? Email the course staff (firstname.lastname@example.org).
Description: Reinforcement learning (RL) is about using machine learning to not just mimic past predictions, but instead to determine which decisions will lead to good, long-term outcomes. While RL is typically viewed and taught from the perspectives of control theory and stochastic optimization, this course will study RL through the lens of probabilistic inference. This perspective will provide new ways of thinking about RL methods and suggest how to build new RL methods using techniques from other areas of machine learning (e.g., self-supervised learning). The course will be split between interactive lectures and discussions of recent papers.
This course is primarily intended for PhD students studying topics related to reinforcement learning and probabilistic inference. That said, the course is open to any students who excelled in CS 324.