CSCE 478/878 Project Rules
- Before you begin your project, you are to submit to me,
in text format in the body of an email, a 2–3 paragraph
proposal of your project. This is due by
Sunday, October 12
at 11:59 pm. You should outline the scope of your project (i.e. what
the problem is), what you plan to do for your project, and a few ideas of
sources (beyond just using the textbook). I will respond soon after with my
assessment of your project (i.e. whether or not you may do it) and some
suggestions on the scope and possible sources.
- Projects are due by
Wednesday, December 10
Monday, December 15.
- By the project deadline you are to submit, via the
web-based handin program:
- A project writeup (in pdf format) of approximately 10–15 pages (more on this below)
- The slides from your presentation (in pdf or PowerPoint)
- On-line copies of the papers you cite in
your report, in .ps or .pdf format.
- All source code and data (in text format)
- Your writeup should include the following sections (some may be
very short or nonexistent, depending on the scope of your project).
- Problem definition
- Related work
- Your approach(es)
- Experimental results
- Conclusions and ideas for future work
- If your project is an implementation, you need something for each
of the above items. For those doing an overview, the "Your approach(es)"
section will be replaced by an expanded "Related work" section, where you go
into more detail on some of the approaches you are reviewing. Also, your
"Experimental results" section will summarize the experimental results of some
of the papers you review.
- A note on "Related work": Your project must include a
bibliography of at least 2–4 papers, and a brief discussion (one page is
plenty) of their content and relevance to your project. EVERY
project is expected to have a section of this kind, even if it is an
implementation. (You can review how others have attacked this problem
[including citations] and give a brief review [and citations] on the
approach(es) you're considering.) If you do not know of related papers
yet, then you might try browsing relevant sections of the text, and/or
look through recent conference proceedings such as the International
Conference on Machine Learning, and recent journal articles in the
journals Machine Learning and Journal of Machine Learning
Research. You may also cite the textbook if
it gives a good overview of something relevant to your project, but you
can do just as well by reading the papers the book cites.
You should also look at
Tips on Presenting
Technical Material since you will be heavily graded on oral and written
presentation of your results.
Last modified 02 December 2014; please report problems to
sscott AT cse.