Spring Semester 2004
Course Projects for CMPT 882, Machine Learning
Below I list a number of suggestions for course project. You are free to propose your own, but you should clear it with me. Once you have some idea of what you want your project to be, you should come see (preferably during office hours) so I can give you more guidance. My general expectation is that the project should take you 20-25 hours.
1. Learning conjunctive causes. In some learning problems, we may assume that there is a set of conditions that (deterministically) causes an effect, but we don’t know which set it exactly is. For example, Maria may get sick after eating a certain combination of foods, but she doesn’t know which – is it the Chinese with the Salad, or the orange juice with the bread? A powerful algorithm for learning the cause was developed in the 19th century by John Stuart Mill, known as “Mill’s methods”. Mill’s methods were reinvented in the 20th century by Patrick Winston in his “arch learner” program. The project is to implement Mill’s methods and run them on a reasonably large data set to test the result.
2. Jasmina Arifovic from our
economics department has a “Turing tournament” for algorithms that learn from
repeated interactions, and that learn to distinguish computer players from
human players. $10,000
3. Some machine learning tools could be enhanced with more features. This would be a service to the community as well. For example, the ANL learner could be enhanced with so-called “boosting” or “bagging” techniques which are widely used for learners in general. The project would be to add an improvement to some existing but not completely mature technology.
4. The Tetrad program from Carnegie Mellon is an implementation of causal inference procedures using Bayes Nets, close to Pearle’s theory. How does Tetrad do as a concept learner, say compared with decision trees?
5. An important concept learning problem in genomics is to distinguish “active” gene sites from “noise”. How do the concept learners we’ve looked at do in this problem? How does Tetrad do?
6. Reinforcement learning methods are based on various probability estimates. A promising idea is that this probabilistic knowledge could be represented by a Bayes net. Then reinforcement learning could be based on inference methods for Bayes net learning. The project is to take a typical reinforcement learning problem (e.g., “grid world”), represent its features (states) in a Bayes net, see how the Bayes net can learn the relevant probabilities, and translate those probabilities into actions. (The last part may be optional if the other parts are difficult enough.)
7. Try to build a reinforcement learner for some problem of interest to you. How about a reinforcement learner for “mine sweeper”, along the lines of the TD backgammon program? Along the lines of problem 4, you could try using a Bayes Net for this purpose.
You could work out a number of exercises (let’s say around 30) to get some in-depth familiarity with the theory/mathematics of learning theory in general or a particular aspect of our course.