Fall 2019 - CMPT 983 G100
Special Topics in Artificial Intelligence (3)
Class Number: 9705
Delivery Method: In Person
This course introduces fundamental concepts in robotics and related fields, including analytical methods for decision making, and machine learning in the context of robotics. Topics include modeling and simulation of robotic systems, optimization, optimal control, robotic safety, reinforcement learning, and robotic perception. Applications of the material include unmanned aerial vehicles and self-driving cars.
- Modeling and simulation
- Optimal control
- Robotic safety
- Reinforcement learning
- Robotic perception
The course grade will be based on homework assignments, a project, and exams.
Introduction to Autonomous Mobile Robots,
R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza,
S. M. LaValle
Cambridge University Press
S. Boyd and L. Vandenberghe
Dynamic Programming and Optimal Control
D. P. Bertsekas
Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS