Princeton University
Computer Science Department

Computer Science 511
Theoretical Machine Learning

Rob Schapire

Spring 2008


General Information | Schedule & Readings | Assignments | Final Project

Proposal due:  Thursday, April 3.
Final report due:  12 noon on Tuesday, May 13.

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.  Working as a pair is very much encouraged; it is assumed that those working individually will not be able to accomplish as much.

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 Thursday, April 3.  Don't hesitate to come talk to me about your ideas.

This project is due at 12 noon on Tuesday, May 13.  Please make every effort to turn in your project on time.  "Free" late days cannot be used for the final project.  Extensions beyond this due date will only be given for genuine and unforeseen emergencies, and may require a dean's signature.

I strongly advise starting early on your project.  Running experiments takes time, as does thinking about theoretical problems.


Choosing a topic

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.

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.  Within this repository, there are many, many datasets to choose from.  There are also other more specialized repositories, such as this one for textual data.  Some of the datasets in these repositories 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.  However, whatever data you use, it should go without saying that if you plan to use data that is private, confidential, classified, copyrighted, controlled, sensitive, etc., it is your responsibility to be sure that it is legally and ethically okay for you to use the data for the purposes of this project (including possibly sharing the data with the COS511 course staff, should the need arise).  Please do not use any data in any way that might be considered illegal, unethical, immoral or inappropriate.

If you are doing a theoretical project, it may be that you read a paper, try improving it, and are not 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 and its mathematical foundations.  Needless to say, turning in a project based on previously completed research would not be appropriate.


Writing a final report

In every case, the end result of your project should be a written report clearly and concisely describing what you did, what results you got and what the results mean.  If you are working individually, your report should be 3-5 pages long.  If you are working with a partner, the two of you together should submit a single report, which may be 4-7 pages long.  The report should use 12pt font, 1-inch margins, and single spacing. The page length limits do not include figures or citations.  Papers that vary from these guidelines risk receiving a grade deduction and/or some sections not being read.

Your report must be submitted in hard copy to the envelope outside my office by 12 noon on Tuesday, May 13.  Although it should not be necessary in most cases, if you wish, you can also submit other electronic materials on a CD that you provide along with your report.

Your report should follow the general format of a scholarly paper in this area.  You should write your report as clearly as possible in a manner that would be understandable to a fellow COS511 student.  In other words, you should not assume that the reader has background beyond what has been covered in class (as well as a general computer science background).

Your report should begin by describing the problem you are studying, some background (what has been done before) and the motivation for the problem, i.e., why it is worth studying.  Previous work and outside sources should be cited throughout your report in a scholarly fashion following the style of academic papers in this area.  (See the proceedings of some of the conferences referenced above for examples.)

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 can 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.  Again, do all this in a way that would be understandable and interesting to a fellow COS511 student.  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?


What you will be graded on

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.