The projects are a significant part of your grade in this class (25%), and the best way to explore and apply the material covered. The key to a successful project is to start early and keep a steady pace throughout the semester. Projects can be done individually or in teams of two.
CIS 620 - Advanced Topics in AI - Spring 09
Probabilistic Graphical Models
- Important due dates:
- Proposal: 5pm on Wednesday, March 4th.
- Milestone: 5pm on Wednesday, April 8.
- Poster: In-class on Monday, April 27.
- Report: 5pm on Monday, May 11.
The hardest part is picking a project topic. Ideally, you will pick something exciting to you, possibly related to problems you already know and care about. Typical projects fall into the following categories (and amazing projects combine aspects of several):
Some project topics from
are a good guide.
You can also take a look at
models course project suggestions.
- Application: Apply known graphical model techniques to a novel task, or area that interests you. This often involves a large component of data collection and analysis.
- Assessment: Take several known algorithms/techinques and carefully compare them experimentally and/or theoretically on several standard (or novel) problems and datasets.
- Algorithm: Develop a new inference or learning algorithm for graphical model problems. Often this results from combining ideas from several known algorithms in an interesting manner, or "lifting" techniques from other areas of computer science, statistics, physics, etc.
- Analysis: Theoretically analyze and prove interesting properties of a known algorithm.
Please arrange to talk about project topics with me before submitting your proposal. Take a look at some recent graphical model research papers for inspiration.
UAI (http://www.auai.org/) and
NIPS (http://nips.cc/) are two of the main computer science conferences where graphical models work is published, with many of the recent papers online. Of course, conferences on computer vision, natural language processing, computational biology, sensor networks, and many others have a lot of graphical model papers as well.
- Project grade breakdown:
- Proposal: 10%
- Milestone: 25%
- Poster: 25%
- Report: 40%
Project proposals should be emailed to me. Please send it in simple ASCII format, no attachments. Include the title of the project, and the name/email of other person in your group if you are a group of two and about 250-500 word description of the project, including the problems/tasks, algorithms/techniques, datasets/resources, relevant papers/references you plan to use/address. Also include what challenges you expect to arise and what you plan to accomplish by the milestone submission. Please send one email per group.
Project milestone reports should be emailed as PDF attachments. The length of report should be at most 3 pages. Please include the names of the team members and the title the project. The milestone report is designed to help you keep on-track and for me to provide feedback and advice. Describe your progress so far and your plan to finish. Keep it brief and to the point: enough for me
understand what you are doing, assuming the material covered in class as background knowledge. Hopefully, you can reuse most of pieces the milestone report as a part of the final report.
We will have a poster presentation in class on Monday, April 27. Each team should prepare a poster and be ready to give a short presentation in front of the poster. The poster session is a great opportunity for you to see other people's projects and get some last feedback before the final report.
Final project reports should be emailed as PDF attachments. The reports should
be at most 8 pages. I recommend using a conference latex template from NIPS or
UAI, or any other conference you like.
The format of your report should resemble a conference paper, with a general outline of the form:
I will evaluate your projects according to the following four criteria:
I plan to post all the final reports online so that you can read about other
projects. Let me know if you do not want your report to be posted.
- Soundness: Are the claims technically correct and techniques and approaches reasonable for the problem?
- Significance: Is the problem addressed important and/or interesting?
- Novelty: Is there something new and interesting about the project (novel application, algorithm, analysis, evaluation)?
- Clarity: Is the presentation clear and concise, but complete enough for someone familiar with graphical models and machine learning?