You are asked to carry out an original research project related to the course content. This description is intentionally very broad. Some possibilities include:

Collaboration

You should form teams of 2-4 students. The team does not have to be the same as for the paper presentations. Your final report should list the contributions of each team member.

Solo projects are allowed but not encouraged, and the final report may be graded by a TA rather than the instructor. The standards will be the same as for a group project.

Project Proposal (due 27 February)

Each team should write a short (1-2 page) research project proposal. It should include a description of a minimum viable project, some nice-to-haves if time allows, and a short review of related work. You don’t have to do what your project proposal says — the point of the proposal is mainly to have a plan and to make it easy for us to give you feedback. (But if you want to completely change direction, please run it by me first.)

Please submit your proposal directly to me via slack.

Final Report and Source Code (due 8 May)

At the end of class, you’ll hand in a project report, in the format of a machine learning workshop/conference paper in NeurIPS format. We recommend the report be about 6-8 pages plus references, but we do not enforce any minimum or maximum length.

Please submit your project report and source code directly to me via slack.

Marking

80% of the marks will be given for meeting the requirements of the project and for the quality of the project proposal, presentation, and final report. This includes:

  1. Abstract (5 points) that summarizes the main idea of the project and its contributions.
    1. Should be understandable to anyone in the course
    2. You don’t need to say everything you did, just what the main idea is and one or two takeaways.
  2. Introduction (10 points) that states the problem being solved and why we might want to solve it.
  3. Figure or diagram (10 points) that shows the overall idea. The idea is to make your paper more accessible, especially to readers who are starting by skimming your paper.
  4. Formal description (15 points) of the model / algorithm / conjecture. Include at least one of: (a) an algorithm box, (b) equations describing your model, or (c) a theorem or formally stated conjecture. Highlight how your approach differs from existing work.
  5. Related work section and bibliography (15 points)
    1. If your project builds on previous work, clearly distinguish what they did from what your new contribution is.
    2. Also, include a short (2-3 sentence) summary of other closely related papers.
  6. Comparison or demonstration (15 points). Include at least one of
    1. A proof of a theorem or conjecture, or an interesting counterexample
    2. An experimental comparison of the results of your method compared with a baseline
    3. An experiment demonstrating a property that your model has that a baseline model does not. Experiments should also include a description of how you prepared your datasets, how you trained your model, and any tricks you used to get it to work.
    4. An experiment that reveals interesting properties of or relationships between existing methods.