ai seminar - russ greiner

  • University of Alberta 3-33 Computing Sciences Centre Edmonton, AB Canada

"introduction to bayesian belief nets"

Bio: After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science and the founding Scientific Director of the Alberta Innovates Centre for Machine Learning (now Alberta Machine Intelligence Institute), which won the ASTech Award for "Outstanding Leadership in Technology" in 2006. He has been Program Chair for the 2004 "Int'l Conf. on Machine Learning", Conference Chair for 2006 "Int'l Conf. on Machine Learning", Editor-in-Chief for "Computational Intelligence", and is serving on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence) in 2007, and was awarded a McCalla Professorship in 2005-06 and a Killam Annual Professorship in 2007. He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes. The main foci of his current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability.

Abstract: Many tasks require building and using a model --- eg, to relate a patient's disease state with the possible symptoms, underlying causes, and effects of various treatments. These relationships are often probabilistic; eg a disease will often, but not always, manifest certain symptoms. Bayesian Belief Nets (BNs), which provide a succinct way to represent such probabilistic models, are in routine use for a wide range of applications, including medicine, bioinformatics, document classification, image processing and decision support systems. This presentation provides a quick overview to BNs: first motivating BNs in general, then describing how BNs exploit "independencies", and finally (if time permits) suggesting ways to learn a BN from data.