Grading Criteria and Submission

Marking Scheme:

• Presentation. 30%. Clarity, conciseness:quality of exposition.
• Originality. 40%. To what extent were you creative in developing your own ideas?
• Evaluation, Methodology. 30%.

I will add further clarifications as required by the students. Submission Items:

• The main item is the final presentation on the designated final presentation day(s).
• Optionally a group or a subset of the students can submit a written report (at most 8 pages single-column, 4 pages double-column).

Topic Suggestions

Here are some suggestions for course projects. Feel free to suggest your own. I suggest discussing the topic with me before you start in any case.

The typical project would evaluate a deep learning method on an interesting problem of your choice. "Interesting" means some mix of real-world importance and learning complexity. I assume that many students will already have a learning problem that they are working on, so it is natural to try deep learning methods. I'm also open to other types of projects, for instance surveys or theoretical analysis. Below I list example topics.

Compare alternative training Methods for graph representations

1. random walks
2. graph neural network
3. node representation vs. graph pooling
4. different hyper parameters within a method
5. loss functions: max-margin, triplet, likelihood

Surveys

• Literature Review
• Theoretical Analysis