UI Wordmark

CS 598 NJ - Special Topics - Stat Reinforcement Lrng

Campus: Urbana-Champaign

Description:

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Special Instructions:

All class meetings will be online and synchronous. Description: Theory of reinforcement learning, with a focus on sample complexity analyses. The course will provide the necessary background and the mathematical tools for understanding the statistical properties of RL algorithms and the challenges. Specific topics include: (1) MDP basics, (2) finite sample analyses of batch RL (tabular and func approx), (3) state abstractions, (4) importance sampling, (5) PAC exploration (tabular and func approx), (6) Intro to POMDPs and PSRs. Prerequisites: probability and statistics, linear algebra, and basic concepts of machine learning. Some familiarity with Markov chains and numerical analysis are also recommended. For more info, refer to the course website for Fall 2018 (on instructor's homepage). For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 4

Term(s): Fall , Spring


Home

Bachelor's Degree

Master's Degree

Doctoral Degree

Certificates

Continuing Education

Search Programs

Search Courses

FAQ

Contact Us


University of Illinois Online
Phone: (866) 633-8465 - Join Us  Facebook
© Copyright 2015 - University of Illinois

Cookie Settings