The intent of the course project is to give you some practice at doing research. If you are a new graduate student, this could be your first time doing research. The important thing to learn is the correct methodology for doing research.

The key components, and those on which you will be graded, are:

• Choosing the right problem. Be creative, find a dataset. Ideally you will have a problem from your personal interests or current/potential research area which could benefit from the use of machine learning techniques. Please feel free to use this problem for your project. However, you must not submit work you have done before this course as your project.

If you haven't decided on a research area, or would like to work on something different, that is fine too.

Don't choose something that is too hard nor too simple. If in doubt, please come to office hours and ask about your topic. A rough guideline for projects is that they should be approximately 2 times as much work (per person) as one assignment.

• What has been done before? A month in the lab can save you a day in the library. This is a course project, and not a peer-reviewed paper, but you should be aware of the most closely related work. Creativity is valued, though a good project can be to implement (part of) a previous paper. I expect roughly 3-5 citations to other work as part of your project report. You must also maintain high standards of academic integrity. Standing on the shoulders of giants is highly recommended, just make it clear who these giants are. If you use someone else's code, you must provide a citation. If you use text/equations from someone else's paper, you must cite and quote it. If you use figures from another paper, you must clearly state such.

• Comparative experiments. You must compare what you have done to at least one other method to know if anything interesting has been achieved. Proper experiments should only change one component at a time (e.g. different classifier, same features). You should also study different parameters of algorithms to ascertain sensitivity (e.g. regularization parameter values). If you are using a standard dataset, you can compare your results (one method) to others'. Just make sure the experiments are comparable (e.g. same training/test data).

You will not be graded only on the quality of your results; high emphasis is placed on the quality of your experimental methodology.

• Quality of exposition. If you write a paper and nobody can read it, does it make a contribution? Clearly state the problem you worked on, the methods you used, who has done what before, what was the intent of your project, which datasets, and what parameters you used. Use a spell-checker, create figures with legible fonts and labelled axes, and provide figures visualizing your results.

A standard project report has four sections:

1. Introduction (includes citations to closely related work)
2. Approach
3. Experiments
4. Conclusion

This material copied from Greg Mori from CMPT 419/726 Fall '19