Statistical Machine Learning: Theory and Applications


for Members

Statistical Machine Learning: Theory and Applications

Wednesday, April 25, 2012

University of Pennsylvania, Berger Auditorium, Skirkanich Hall

Presented By


Over the course of the last few decades a vibrant field of research has emerged at the nexus of statistics and computational science. Statistical machine learning has given rise to techniques that are currently used to analyze, quantify and predict phenomena in a wide range of fields. Algorithms and ideas developed in this area have been employed in a vast number of applications including face and speech recognition, web and image search, customer preference prediction and genomic exploration. This symposium honors Prof. Vladimir Vapnik, for his fundamental contributions to our understanding of statistical machine learning, and to the development of important machine learning methods.


Michael Kearns

University of Pennsylvania

Robert Schapire

Princeton University

Michael Jordan

University of California, Berkeley

Vladimir Vapnik

Columbia University & NEC Laboratories

This meeting is free and open to the public, but RSVP's are appreciated.

Presented by

  • The Franklin Institute
  • GRASP Laboratory
  • Penn Research in Machine Learning
  • NYAS

Travel & Lodging

Meeting Location

University of Pennsylvania

Berger Auditorium, Skirkanich Hall
210 South 33rd Street
Philadelphia, PA 19104