CS534 Final Project


Overview
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

Note that all submissions pertinent to the project (including proposal, milestone, and final report) should be submitted through email to the instructor (xfern@eecs.oregonstate.edu). Please include "CS534-12 Project - XXXX" in your email title, where "XXXX" specify whether it is a proposal, milestone or final report.

How are projects evaluated:

Projects will be evaluated based on:
  1. 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 algorithms?
  2. 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?
  3. 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.
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.


Project Topics
Projects most often can be grouped into one of the two categories below:
  1. 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.
  2. 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.
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.
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 ---http://dl.acm.org/citation.cfm?id=2020408&coll=DL&dl=ACM&CFID=98900497&CFTOKEN=83398856). The proceedings of ICML 2011 can be found at (http://www.icml-2011.org/abstracts.html).


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.