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Machine Learning Laboratory
Canada Research Chair in Statistical Learning Algorithms
NSERC-CGI Industrial chair on high-dimensional data mining for e-Finance
Machine learning in adaptive computer systems is an area of research at the intersection of artificial intelligence, statistical inference and optimization. Such systems must learn to perform a task from a set of examples and a priori knowledge about the task. These systems are often also adaptive, i.e. can adapt to changes in their environment. Several of the algorithms used in our laboratory are from research in artificial neural networks, inspired from the way in which the brain processes information.
Machine learning algorithms are important when one does not have enough explicit knowledge about the problem to directly write a program or a set of rules that will solve the task, or when we don’t really know the form of the true distribution from which the observed data are coming from. In some cases one can obtain more information about the problem in the form of examples of the task to be performed, usually a set of (input, output) pairs (e.g. of patterns, or sequences). These examples tell the system what it should produce in output in a certain situation represented by its input, or what joint values are plausible for the observed variables. These examples must then be combined with explicit knowledge about the problem to design the system architecture and representation, and choose an appropriate learning algorithm.
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