What we’re about
Hands-on project-oriented data science, with a heavy focus on machine learning and artificial intelligence. We're here to get neck-deep into projects and actually do awesome things!
Join us on slack! https://join.slack.com/t/boulderdatascience/shared_invite/zt-20t147vcy-SIo7is6meTWOfHa1ENlJeA
The meetup consists of:
- recurring study groups (if you want to start one, just notify Ben to be made a meetup co-organizer).
- intermediate/advanced working groups (starting in 2019)
- occasional talks and gathering (aiming for at least quarterly starting in 2019)
Upcoming events (1)See all
- Reinforcement Learning Chapter 5 Part 1Link visible for attendees
Last meeting we finished Chapter 4 on Dynamic Programming. For this meeting, we will start Chapter 5 on Monte Carlo methods for evaluation and control and try to get through section 4.4 and exercise 4.4. "On Policy" evaluation and control are the subjects of this part of the chapter which means that we are generating samples from an agent interacting with an environment following the policy of interest. We can also discuss the Monte Carlo sampling approach and how it differs from Dynamic Programming in terms of efficiency and convergence.
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. The full book and supplemental material is available here: http://incompleteideas.net/book/the-book.html
My exercise solutions and chapter notes:
Kickoff Slides which contain other links: https://docs.google.com/presentation/d/1QD3iw5BgIpPpl_K_ApAlDr1NRseR1WmXme1dKQGqTOg/edit?usp=sharing
Video lectures from a similar course: https://youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm