The fee includes extensive materials, breakfast items, lunch, and refreshments for AM and PM breaks. Space is limited. To ensure yourself a place, please register early.
Space is limited. To ensure yourself a place, please register early.
Online Registration: https://www.123signup.com/calendar?Org=chicagoasa
Nonmembers, join the chapter for a year for only $15 and get the discount plus all the benefits of membership!
Contact Tony Babinec, VP Workshops (firstname.lastname@example.org).
Vladimir Cherkassky is Professor of Electrical and Computer Engineering at the University of Minnesota, Twin Cities campus. Dr. Cherkassky is co-author of Learning from Data, now in its second edition, and he has also authored the forthcoming Introduction to Predictive Learning.
He has been recently selected as Fellow of IEEE, for ‘contributions and leadership in statistical learning and neural networks’. He has served on editorial boards of IEEE Transactions on Neural Networks (TNN), Neural Networks (the official journal of INNS), Natural Computing: An International Journal and Neural Processing Letters. He was a Guest Editor of the IEEE TNN Special Issue on VC Learning Theory and Its Applications published in September 1999. Dr. Cherkassky was organizer and Director of NATO Advanced Study Institute (ASI) From Statistics to Neural Networks: Theory and Pattern Recognition Applications held in France in 1993.
He received the IBM Faculty Partnership Award in 1996 and 1997 for his work on learning methods for data mining. In 2008, he received the A. Richard Newton Breakthrough Research Award from Microsoft Research for development of new learning methodologies for predictive learning.
Note: Our original speaker, Lutz Hamel of the University of Rhode Island, regrets that he is unable to present his SVM workshop.