I’m re-posting the capstone message because we accidentally left off the Algorithmic Game Theory capstone this fall. If you want to take that course, please go back into the survey to submit your answers again. You should be able to resubmit by going to the survey linked on the capstone page. Information about the Game Theory capstone is linked there as well.
We’ll send a separate email soon about how to apply for the animation project course series.
The capstone registration form is now open :
http://www.cs.washington.edu/education/ugrad/academics/capstone.html I’ve posted information below on the robotics capstone, every other course should have a link to previous course offerings. Most capstones require you to have completed at least CSE 332, and one or two 400 level courses as the goal is to “cap” your CSE experience. However, some faculty are more flexible than others. So you can always put your name down and then talk with the faculty as the course approaches.
First registration priority will go to students graduating. We’ll attempt to give everyone who fills out the survey their first or 2nd choice capstone. If there is still space available, we will open it during Junior registration for any students who did not register during this time period to add a capstone, or to take a 2nd capstone.
We’ll send results around June 10th. Add codes will be sent out during that quarter’s registration period.
2011-2012 Capstones:
CE Hardware Track Capstone:
- Spring: 477: Hardware Systems
CE Software Track Capstones:
- Fall 481a: Robotics (Rao)
- Fall 490: Algorithmic Game Theory (Karlin)
- Winter 454: Advanced Internet Systems
- Winter 481: Accessibility (Ladner)
- Winter 481: Sound Capstone (Hemingway)
- Winter 481: Home Networking Capstone
- Spring 481d: Games Capstone
- Spring 481a: Operating Systems (Kimura)
- Spring 481a: Computational Biology
- Spring 481k: Capstone Design for Low-Resource Settings (Borriello)
CSE 481 Robotics Capstone
The Humanoid Robot Imitation Learning Challenge
Autumn 2011
Course description:
Your mission, should you choose to accept it, is to program a humanoid robot to imitate human actions and learn new skills from human demonstration using video from a Kinect RGB+depth camera. Students will work in groups to tackle the various sub-problems of human motion capture from video, control of the humanoid robot, and application of probabilistic reasoning and machine learning to the problem of learning from human demonstration.