CS 499: Introduction to Intelligent Decision Making
Instructor: Sandhya Saisubramanian
Course Credits: 4 units (Note: The 4 credits from this course can be used towards your degree requirement! Check with your advisor on how to do it.)
Meeting Information: Monday & Wednesday 2:00 pm - 3:50 pm at Holland Hall 202
Instructor Office Hours: TBA
Course Description: What do Alpha-Go, self-driving cars, autonomous robots, and Mars rovers have in common? These systems are capable of making intelligent decisions autonomously. How to design such systems? How to process the available information to make intelligent decisions? How to solve real-world problems using these techniques?
This course will cover some of the key concepts in intelligent decision-making, specifically reinforcement learning. Topics include agent representation, Markov decision process, fundamentals of model-free and model-based decision-making under uncertainty, value iteration, policy iteration, Q-learning, SARSA, actor-critic methods, and imitation learning.
Prerequisites: Students should have taken CS 325 or CS 325H, with a C or higher, before registering
Announcements and Discussion Board: We will be using Canvas for the course. All course-related announcements and readings will be posted on Canvas.
Grading: Assignments: 60%, In-class Quizzes: 15%, Final project: 25%
Statement Regarding Students with Disabilities: Accommodations for students with disabilities are determined and approved by Disability Access Services (DAS). If you, as a student, believe you are eligible for accommodations but have not obtained approval please contact DAS immediately at 541-737-4098 or at http://ds.oregonstate.edu. DAS notifies students and faculty members of approved academic accommodations and coordinates implementation of those accommodations.
Schedule (subject to change):