Open Research Projects

Open Undergraduate or Graduate Research Projects

Projects can lead to a 4th year project or a Master's Thesis

Some funding is available

Project 1: Combining Supervised and Unsupervised Learning

Supervised Learning consists of learning from experience using the outcome of that experience. For example, assume that investing a certain amount of money in a given type of stock under some particular market condition proved very successful since the value of that investment tripled in 3 months. Conversely, assume that other investments proved disastrous as their value shrinked by 40% in the span of one month. The purpose of supervised learning is to discover the rules that, when applied, will allow us to invest properly so as to make a maximum amount of profit, based on previous good and bad experiences.

Unsupervised learning, on the other hand, does not make use of such outcomes, but it learns how to group similar types of data together. It is the type of learning that gaves rise to zoological or botanical classifications: from observing different animals carefully, it became clear after a while that dogs and wolves share similar caracteristics, while cats, lions and tigers form another group. These groups are different from those of birds and so on. Such classifications did not use any type of outcome, but they grew out of studies of these animals' various features. That's what unsupervised learning is all about.

Although closely related and somewhat complementary, Supervised and Unsupervised learning are usually treated as different research areas. Yet, there could be great advantages in combining both approaches. On the one hand, unsupervised learning could be used to study the data prior to feeding it to a supervised classifier. This would have the advantage of helping us tune the supervised learner or the data set in an intelligent way that would help us improve its accuracy. On the other hand, unsupervised learning could be used simultaneously with supervised learning so as to bias the supervised learner in a meaningful way.

The purpose of this project will be to experiment with either or both schemes. After being made familiar with both supervised and unsupervised learning, the student will be given the opportunity to decide which of the two problems, type of classifier and combination technique s/he would like to try. Alternatively s/he could be given more guidance as to how to conduct the project.

Project 2: Learning how to Translate Locative Preposition from English into French from Large Text Corpora

Machine translation of locative prepositions is not straightforward, even between closely related languages. For example, while English speakers will say: "The boy is ON the bus", French speakers will say: ``Le garcon est DANS le bus" (Literally, "The boy is IN the bus"). Similarly, English speakers will say: "The picture is ON the wall" while French Speakers will say "Le tableau est AU mur" (Literally, "The picture is AT the wall"). The purpose of this project is to use statistical techniques and Reference tools such as Dictionnaries, Thesauri, or WordNet to browse through large corpora of bilingual texts and attempt to learn translation rules that will associate the correct French translation to the English locative preposition used in a given context.

Since this project can be managed in two phases (a French and an English phase) separately, although preferable, knowledge of both English and French is not required. After learning the various text processing tools, the student will be given the freedom to choose the direction for his/her project that interests him/her most. Alternatively, s/he will be given more specific guidance.

Project 3: Your Idea

If you have an idea related to Artificial Intelligence, Machine Learning and/or Text Processing, I will be glad to discuss it with you. Hopefully, we will be able to negotiate a topic of interest to us both!