Begin forwarded message:
From: Patrice Marcotte marcotte@iro.umontreal.ca Subject: Fwd: TR: Some documentation... Date: 31 mai 2013 15:30:13 HAE To: employes@iro.umontreal.ca
Veuillez noter un séminaire en apprentissage automatique (machine learning).
COLLOQUE DU DIRO DATE: mercredi 5 juin 2013, 10h00
LIEU: salle 3195, pavillon André-Aisenstadt
CONFÉRENCIER: John LANGFORD, Senior Research Scientist, Microsoft Research
John Langford studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. from Carnegie Mellon University in 2002. Since then, he has worked at Yahoo!, Toyota Technological Institute, and IBM's Watson Research Center. He is also the primary author of the popular Machine Learning weblog, hunch.net and the principle developer of Vowpal Wabbit. Previous research projects include Isomap, Captcha, Learning Reductions, Cover Trees, and Contextual Bandit learning. TITRE: LEARNING FROM LOTS OF DATA
ABSTRACT: In the last decade, sources of data for machine learning have become extremely plentiful, leading to new classes of problems. I will discuss two such problems, whose joint solution is critical to successful learning here:
(1) How do you create learning algorithms capable of coherently dealing with large quantities of data?
(2) How do you learn given the partial feedback nature available in most large scale data sources?
In addition, I'll discuss some open problems which I'd like to solve in these areas.