CS534 Final Project
The goal of this class project is to provide you with
an opportunity to explore an interesting machine learning problem of
your choice in the context of a real-world data set. You
are encouraged to combine machine learning research ideas with
problems in your own research area. Your class project must be about
new things you have done this term, you can't use results you have
developed prior to this class. Your project will be worth 25% of
your final class grade.
Note that, as with any conference, the page limits are strict!
Papers over the limit will not be accepted.
What you need to do
- Choose your application domain and learning problem within it
and form a team to work on this project. (Projects can be done
in teams of up to three people.)
Turn in a project proposal by May 4th. This will give you
approximately 5 weeks to work on the project. Feel free to
submit the proposal early if you have already decided what you
want to do for your project. The proposal should provide the
title of the project, the full names of all of your team
members, and about a 300-500 word description of what you plan
to do. Please send your proposal as a normal email (not as an
attachment) to the instructor. If you wish to do a project
jointly with another project from a different class you are
currently taking (with the consent of the other class'
instructor), your proposal must clearly say so.
that all submissions pertinent to the project (including proposal,
milestone, and final report) should be submitted through email to
the instructor (firstname.lastname@example.org). Please include
"CS534-12 Project - XXXX" in your email title, where "XXXX" specify
whether it is a proposal, milestone or final report.
Turn in a one page milestone report on May 23rd. This is
approximately half way between the proposal and final report
due date. Your milestone report should describe what you have
accomplished so far, and briefly what you plan to do for the
remaining part. The milestone report should be written as if
this is an "early" draft of your final report, and you should
be able to reuse a good portion of this report for your final
Give a final presentation on your project (date to be
determined, most likely the last day of the class). Each team
should prepare a short presentation to describe their work to
the whole class.
- Turn in a final paper (no longer than 8 pages including references,
figures and tables in NIPS
June 8th midnight. Each team should turn in a single
report and please email me your report before the deadline.
How are projects evaluated:
Projects will be evaluated based on:
It should be noted that the best class projects often come from
students working on topics that excite them. So, pick something that
you can get excited and passionate about! If you're unsure what
would or would not make a good project, feel free to email me
or come to my hours to talk about project ideas.
- The technical quality of the work. For example does the
technical approach make sense? Are the things tried reasonable?
Are the experimental evaluations done in a proper and fair
manner? Are the proposed algorithms or applications interesting?
Do the authors reveal novel insight about the problem and/or
- Significance. Did the authors choose an interesting or a
"real" problem to work on, or only a small "toy" problem? Is
this work likely to be useful and/or have impact?
- The novelty of the work, and the clarity of the writeup. Did
the authors clearly describe their work such that the technical
details could be fully evaluated.
Projects most often can be grouped into one
of the two categories below:
One could also develop a theoretical project that focuses on proving
some interesting non-trivial properties of a new or existing
learning algorithm. Projects could also be a combination of these
different types, involving both interesting application domains and
also new algorithms, or theories.
- Application project. For this type of projects, you need to
pick an application that interests you and explore how to best
apply machine learning to solve it. This is typically the most
common type of projects that we see in this class.
- Algorithmic project. This type of projects focus on developing
and testing a new algorithm or some novel extensions of an
existing algorithm for solving some machine learning problem.
This is most appropriate for students who are doing machine
learning research and want to combine their research efforts
with this class project.
For inspirations about possible class projects, you might want to
take a look at some recent machine learning research papers from
machine learning and data mining conferences. In particular, the
International Conference on Machine Leanring (ICML) and the ACM
SIGKDD Conference on Knowledge Discovery and Data mining are good
places to look for such papers. The proceedings of KDD can be
accessed through the ACM portal (for 2011 proceeding
of ICML 2011 can be found at
Some Possible Learning Problems
I am in the process of contact some faculties for potential
projects that are directly related to ongoing research at OSU.
Once I receive such information, they will be posted here.
- Computer Vision Recognition
- Optical Character
- Face Recognition Tasks
- Scene recognition (e.g.
house vs. no house)
- Object recognition in
- See http://elm.eeng.dcu.ie/~oconaire/cv_datasets.html
for a list of computer vision benchmark data sets
- Audio Recognition Tasks
- Speaker identification
- Speaker sentiment
- Music genre
- Bird species recognition by
songs (contact the instructor)
- Text Classification and
- Email Folder Predictor
- Newgroup document
- Sentiment classification
- Author classifier (i.e.
take latex files from different authors and try to classify
according to author)
- There are a few text
classification datasets on Andrew
- gene clustering/expression
- gene sequence analysis