Reading Comments

On the course calendar, roughly half of the assigned readings are marked with a bold (C) symbol. For each of these readings, all students are expected to submit brief comments about its strengths, its weaknesses, and the questions it raises. Your plain text review should have the following format:

The Good
1-3 sentences. What is the most exciting or interesting model, idea, or technique described here? Why is it important? Don't just copy the abstract - what do you think?
The Bad
1-3 sentences. What is the biggest weakness of this method, model, or algorithm? Problems may include weak empirical validation, missing theory, unacknowledged assumptions, applicability to a narrow range of problems, or ...
The Ugly
1-3 sentences. What didn't you fully understand? What would you like to see explained or discussed in class? Feel free to highlight unclear sections, steps you didn't follow, assumed background knowledge you don't have, or ...
Detailed Comments
Optional. Extra credit will be given for detailed analysis that highlights non-obvious strengths, weaknesses, assumptions, or connections. We do not expect or want such comments for every paper, but if the reading inspires interesting ideas please do share your perspective.

All comments should be posted to the course's Google discussion group, brown-cs295-p, by 8:00am on the day that paper is presented. Late comments will not be given credit, but students can skip comments for three readings over the course of the semester without penalty. When posting your comments, please reply to the thread created by the instructor for that particular reading. Comments are moderated, and will be posted for view by other class members after the submission deadline. Use the following form to join the discussion group:

Google Groups
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Reading Presentations

By the end of the semester, each student is expected to lead one or two 40-minute course discussion. Typically such presentations will focus on either a single journal-length paper, or a pair of shorter, related papers. Do not try to quickly go through every detail - instead focus on describing the key concepts clearly.

After class, each presenter should send the instructor the slides or notes used in their presentation. These will be posted on the course webpage.

Final Projects

The final project will count towards 70% of overall grades. Of these points, 10% will be based on a 1-3 page project proposal, due on March 26; 10% will be based on a short oral presentation, given on May 10; and 50% will be based on a technical report describing the results, due on May 12.

Projects which apply graphical models to the student's own research interests are particularly encouraged. Please feel free to discuss potential project ideas with the instructor. Some possible styles of project include:

  • Propose a new graphical model suitable for a particular application, and test baseline learning/inference algorithms
  • Propose, develop, and experimentally test a modification or extension of an existing learning/inference algorithm
  • Experimentally compare different models or algorithms on an interesting, novel dataset
  • Survey the latest advances in a particular application area, or for a particular type of learning algorithm, for which no such survey currently exists

Project Proposals

The project proposal should be at most 3 pages long, including all figures and references. We encourage, but do not require, you to use the NIPS LaTeX style file. Proposals must be submitted as a single pdf file, by e-mail to the instructor, before 11:59pm on Friday, March 26. Your proposal should contain the following information:

  • A clear description of the problem or application you intend to address. Why is it worth studying?
  • A discussion of related work, including references to at least three relevant research articles. Which aspects of your project are novel?
  • Except for literature surveys, an experimental evaluation protocol. How will you know that you've succeeded?
  • A concrete plan for accomplishing your project by the end of the course. What are the biggest challenges?
  • A figure depicting one or more graphical models which play a role in your project. We recommend creating such figures in a vector drawing program, such as Adobe Illustrator, Inkscape, or Xfig.

Project Reports

The technical report should be between 6-12 pages long, in the style of top machine learning conferences. Although novelty need not be sufficient for publication at such conferences, the presentation and experimental protocols should be. We encourage, but do not require, you to use the NIPS LaTeX style file. Reports must be submitted as a single pdf file, by e-mail to the instructor, before 11:59pm on Wednesday, May 19.