CSE515 Statistical Methods in Computer Science – Spring 2013

Homework

Midterms

Midterm 1 is on May 1st, Midterm 2 is on May 29. Midterms are in-class, open book/notes.

Project

Project guidelines

Projects can be done individually or in groups of two.

Topics

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):

• 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 and NIPS 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, and many others have a lot of graphical model papers as well.

Evaluation

Project grade breakdown:

• Proposal: 10%

• Literature Survey: 25%

• Poster: 25%

• Report: 40%

Project proposal

Project proposals should be posted on the wiki. Create a page for your project and link from and to your profiles. Include the title of the project and about 250-500 word description of the project, including the problems/tasks, algorithms/techniques, datasets/resources, at least 3 relevant papers/references you plan to use/address. Also include what challenges you expect to arise.

Literature survey should include 3 papers most relevant to your project and be roughly 3-4 pages. Explain the background, stengths and weaknesses of the approaches, and how they relate to your project.

We will have a poster presentation in class. 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 report

The format of your report should resemble a conference paper, with a general outline of the form:

• Abstract

• Introduction/Motivation

• Methods/Algorithms

• Results/Experiments

• Discussion/Conclusion

• References

We will evaluate your projects according to the following four criteria:

• 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?