University of California at Berkeley
Dept of Electrical Engineering & Computer Sciences
CS287: Final Project Guidelines
Final Project Contents
The final project could be either of the following, where in each case the topic should be closely related to the course:
Ideally, the project covers interesting new ground and might be the basis for a future conference paper submission or product.
- An algorithmic or theoretical contribution that extends the current state of the art.
- An implementation of a state-of-the-art algorithm using real-world data and/or robots.
You are encouraged to come up with your own project ideas, yet make sure to pass them by me before you submit your abstract.
Feel free to stop by office hours or to set an appointment (via email) to discuss potential projects.
Logistics and Timeline
Project presentations will be recorded and videos will be made available online. Final papers will also be made available online.
20 percent of project grade will be based on the quality of the presentation, 20 percent based on the quality of the writing of the final paper, and 60 percent based on the quality of the results themselves.
- 1 or 2 students/project. If you are two students on 1 final project, you will submit the same as 1 person projects, and naturally I will expect twice as much in terms of results.
- Nov 1st: Approved by instructor abstracts due: 1 page description of project + goals for milestone. Make sure to sync with me before then!
- Nov 17th: 2-4 page milestone due. You are not graded on the milestone. Think of it as a sanity check for yourself that you indeed have started to make progress on the project and an opportunity to get feedback on your progress thus far, as well as on any revisions you might have made to your project goals.
- Dec 6th: In-class project presentations.
- Dec 15th: Final paper due. This should be a 4-6 page paper, structured like a conference paper. I.e., focus on the problem setting, why it matters and what's interesting/novel about it, your approach, your results, analysis of results, limitations, future directions. Cite and briefly survey prior work as appropriate but don't re-write prior work when not directly relevant to understand your approach.