Course Policies
Grades will be based on the term project (70%), presentation of
research papers as well as the term project (15%), and class
participation and presentation critiques (15%). You can work on the
project and presentation in teams of no more than two students. You
are required to make a web page for your project and post your
progress on it. For examples of project web pages see here
.
Term Projects
You have the opportunity to design your own project in statistical
machine learning. Think of a cool domain or problem in which you have
data which you can use to build predictive models. The data could be
from the biomedical domain (e.g., reverse-engineering cell signaling
networks from microarray and flow cytometry data from cancer cells),
financial domain (e.g., predicting a company's stock price from data
on the company's performance as well as other general ecomic
indicators), social sciences (e.g., building predictive conflict
models from events data in political science), retail domain (e.g.,
predicting a customer's book preference from knowledge of other
customer purchases), games (e.g., learning models of an opponent's
behavior in a strategy game or in Robosoccer). It can be a problem
you are already working on, or be something completely new. I can
brainstorm with you and suggest ideas for projects. You will need to
write a short proposal outlining the goals of the project, its scope,
a preliminary design, how you intend to evaluate it, and a list of the
final deliverables. More details on the project are here.
Research Presentations
In class, the group whose turn it is, will present the contents of the
readings for the day in about 40 minutes. Notes on how to make an
effective presentation are here. The
presentation will be evaluated by everyone else in the class.
Presentation evaluation forms are here. The
instructor will try to keep the post-presentation discussion fruitful
and make sure that all students get a chance to participate. However,
it is the group's responsibility to jumpstart the discussion. To
prevent the discussion from languishing, you may want to prepare some
backup questions, and/or make controversial statements, to keep things
going. While the presentation is the responsibility of the presenting
group, it is everyone's responsibility to do the assigned reading for
the class and contribute to the discussion at the end of he
presentation.
Handouts
Optional homework problems will be posted on the course Web page.
Annoucements pertaining to the class will made on the course
newsgroup, so please check it frequently. Presentation slides will be
posted on the web too. You need to mail me your Powerpoint slides
before class.
Academic Integrity
The work you submit for this class is expected to be the result of
your own work and that of your team mate. You are free to discuss
course material and approaches with your other classmates, the course
assistants and the professor, but you should never misrepresent
someone else's work as your own. It is also your responsibility to
protect your work from unauthorized access. We expect you to follow
the Honor Code in this course.
Accomodation for Disability
If you have a documented disability that will impact your work in this class,
please contact me (devika@rice.edu) to discuss your needs. Additionally, you
will need to register with the Disability Support Services Office in the Ley
Student Center.
Last modified: 5 January 2008 by
Devika Subramanian
devika@rice.edu