Computer Science 511
Proposal due: March 30.
Final report due: May 11.
The final project for this class is completely open ended. You can pick just about any topic you wish so long as there is some connection to machine learning and its mathematical foundations. For your project, you can run an experiment, or you can think about a theoretical problem or algorithm, or you can do a blend of both. You can work individually or with a partner, although larger groups need to be justified by larger projects.
Please email me (in plain text) a paragraph or two outlining your project as soon as you know what you want to do, but no later than Tuesday, March 30. Don't hesitate to come talk to me about your ideas.
This project is due on Tuesday, May 11. Please make every effort to turn in your project on time. "Free" late days cannot be used for the final project.
I strongly advise starting early on your project. Running experiments takes time, as does thinking about theoretical problems.
For your project, you should start by doing some reading on a topic, and then you might run an experiment, or try to simplify or improve or extend the result, or you might try applying an algorithm to a particular application, or you might think about how two different approaches or algorithms are related to each other. Or you can do something different from any of these.
In every case, the end result should be a 5-10 page report clearly describing what you did, what results you got and what the results mean. I would prefer to receive your report in hard copy. However, depending on the project, you might also find it appropriate to email me other materials. It is very important that you get everything to me on time so that I can turn in final grades on time. In addition, I would like each of you to give a short (say around 10-minute) presentation to the class about what you did. Presentations will be scheduled for the very end of the semester.
Examples of possible types of projects:
Places to look to get ideas for topics:
You may use software that you find on line. If you do, please note this in your report, and, as with any project, demonstrate in your report that you understand how the underlying learning algorithm works. If you implement code yourself, be aware that it can be tricky to be sure that a machine learning program is actually working properly. Be sure that it is carefully tested before running your experiments. For instance, check the output of the program carefully on tiny datasets where you know what the output should be (for instance, you have computed it by hand, or you have found or implemented another program (say, in another language or using a different technique) that computes it for you). Also keep an eye out for clues that your program might have problems, for instance, if the results violate proven theorems. Your report should describe briefly what measures you took to be sure that your program is working properly.
One of the best places for obtaining "real" data is the repository at University of California, Irvine (click on "summary page", or follow links to explore some of the other machine learning resources available from this site). Within this repository, the "statlog" datasets have been widely used, as has the "letter recognition" dataset, but there are many datasets to choose from. Some of the datasets have separate test sets. Others only provide a training set. In this case, you can randomly partition the dataset into a training set and test set. If you end up with a rather small test set, you will probably want to repeat this many times to get reliable results. You can also use synthetic data of your own creation, in which case there is no problem generating a large test set. Usually, when evaluating a machine learning algorithm, you will want to see how it performs on several datasets. If you have access to more specialized data (for instance, as part of your regular research), feel free to use it.
If you are doing a theoretical project, it may be that you read a paper, try improving it, and aren't able to make progress. In that case, it is okay to fall back on just explaining the paper as clearly as you can, in your own words.
It is okay to do a project that is related to your primary research. In this case, you will need to carve out a project that is focused and relevant to machine learning. Needless to say, turning in a project based on previously completed research would not be appropriate.
Your report should follow the general format of a scholarly paper in this area. You should begin by describing the problem you are studying, a bit of background (what's been done before) and the motivation for the problem, i.e., why it's worth studying.
Next, you should clearly explain what you did, both from a high level, and then in more detail. For an experimental paper, you should explain the experiments in enough detail that there is a reasonable possibility that a motivated reader would be able to replicate them. You also should outline some of the theory underlying the algorithms you are studying. State your results clearly, and think about graphical tools you could use to make your results clearer (a table of numbers is usually less compelling than a graphical representation of the same data). Look through published papers for ideas. For a theoretical paper, the learning model and other mathematical details should be explained well enough for the results to be stated with mathematical precision and clarity.
In every case, be sure to explain the meaning of your results. Don't just give a table of results or a dry mathematical formula. Explain what the results mean, and what conclusions can be drawn from them. What did you expect to find? What did you find instead? What are the implications? If you found something surprising, can you think of how it might be explained?
Projects will be graded along the following dimensions:
As always, feel free to contact me anytime with questions or difficulties you encounter, or if you have trouble thinking of a topic or finding papers to read.