EECS 545 Final Project Report Guidelines

The final report for your 545 project is due Thursday, Dec. 17, at noon. It should no more than 10 in length, including figures and references, and should follow the same formatting guidelines as the proposal and progress report (12 pt font, 1 inch margins). The bibliography may be in 10 pt. font. Please don't use visual aids that rely on colors to convey information; I have certain kinds of colorblindness, e.g., red-green. Please submit both

• a hard copy under my door
• an electronic copy in pdf or word format, attached to an email with subject "EECS 545 final project report".

The report should represent the complete work for your project. In other words, I am not next expecting you to perform additional investigations for the poster. In all likelihood, I will have graded your reports before the poster session.

Your report should be written to an audience of one (me). Therefore you need not provide lengthy explanations of background material on methods we covered in class. On the other hand, even if you know I am familiar with the literature in a certain area, I expect to see proper discussions of related work with citations.

As a reminder, the project will be evaluated as follows (from the project proposal description): "Your project should be an open-ended investigation into some area not covered in the course. Furthermore, every project should make some original contribution to machine learning methodology, the application of machine learning to real world problems, or both. Projects will be graded based on originality, execution, and the quality of the poster and written presentations. Your project does not need to be publishable research, but you cannot simply redo what someone else has already done. There should be some degree of exploration and innovation."

Every report should address the following (but don't feel obligated to make these the section heading)

• Problem statement.
• Motivation: why is the problem important.
• Related work.
• Your contribution/innovation.
• Methodology developed in the project
• Evaluation: experiments on sythetic and real data, performance measures used, results.
• Conclusions: what did you learn, what were the project's success and failures?
• Description of individual effort: At the end of the report, please include a brief (2-3 line) description of each project member's contribution to the project.

Please keep the following points in mind

• Define all terms and variables! Keep in mind that even if a term is a very simple concept in a certain application domain, do not assume I'm familiar with it because in many cases I won't be.
• As you are prioritizing your work, think about this: I'd rather see you be thorough in your examination of a few issues than shallow in many.
• Even if different group members worked more or less independently on different parts of the project, the final report should read as a unified whole. How did the different parts relate to and inform the others?
• I will weigh the quality of your prose in making my evaluation. I also encourage the use of figures to make concepts visual and concrete. Such figures will be useful for the poster, so the effort will be worth it.
• Figure captions should be pretty much self contained.