Guidelines for the Final Report
Due Date: Last Day of Classes
Page Limits: n <= 25
(from the graduate Machine Learning course Nathalie Japkowicz taught at Dalhousie
Here is a set of guidelines which, I hope, will assist you in the
writing of your final project report. As a general rule, your report
should be understandable by anyone with a reasonable understanding of
machine learning but who doesn't know the particular approaches or the data
that you used. As well, as you write, you should try to imagine that
you are conversing with a very interactive reader (who doesn't know
anything about your project but who wants to find out everything!).
Try to be this reader and to guess all the questions s/he would ask
you and all the challenges s/he would have for you. Then incorporate
your answers to these questions and challenges in your report so that,
hopefully, you will have pre-empted many of your (real) readers' questions.
In addition to the introduction and conclusion (which can be thought of
as summaries of your study directed at a general audience, but with more
emphasis on your motivations in the case of the introduction and more
emphasis on your results and their implications in the case of the
conclusion), your report should contain:
- A statement of the problem you are studying.
- A review of the related literature on the topic and a discussion of
where your study fits in this previous literature.
- A description of the method you have designed or of the methods
you are comparing. Assume that the reader does not know how the systems
you have designed and/or used work.
- A description of the data to which you applied your research
(this description should include: number of features, values these features
can take, size of the data set, size of the training and testing sets, etc.)
- A description of the methodology you used to set the various learning
parameters of the systems you tested and a discussion of the optimal
settings you found. This is particularly relevant in the context of
Neural Networks, for example where the Number of Hidden Units,
Learning rates, Momentum, Number of RBF's etc. have to be chosen by the user.
The idea here is that your results should be reproduceable by anyone reading
- A description of your testing methodology (e.g., 10-fold cross-validation)
and a discussion of why this testing methodology is appropriate.
- A description of your results. Think of the format that would best
illustrate the points you are trying to make. Should you list your results in
a table? represent them with a graph? what sort of graph? what results are
necessary to report?
- A discussion of your results. i.e., a section that explains why, in your
opinion, the results you reported were obtained: why the learners you
considered were successful or why they failed. If you want, you can also
discuss what you think would happen under conditions different from those
you specifically tested.
- A discussion of the relevance of your results: what have you achieved
with your study? How do your results support the claims you have made
in the earlier parts of your report?
- A section discussing future work. There, you should try to identify sets
of experiments that would be interesting to run and to discuss why
they would be interesting (i.e., what are the issues that such experiments