Tutorial 2

By: 

Prof. Abdallah Shami, Western Ontario University, Canada

Abstract:

The objective of this tutorial is to introduce Machine Learning techniques based on a unified, probabilistic approach. Regression, classification, clustering, neural networks, mixture models, ensemble methods, and structure prediction will be covered in this tutorial. In addition, this tutorial will present a series of practical engineering case-studies (i.e., Network Security, eLearning Engagement, and others).

About the Speaker:

Dr Shami received the B.E. degree in Electrical and Computer Engineering from the Lebanese University, Beirut, Lebanon in 1997, and the Ph.D. Degree in Electrical Engineering from the Graduate School and University Center, City University of New York, New York, NY in September 2002. Since July 2004, he has been with Western University, Canada where he is currently a Professor and Graduate Chair at the Department of Electrical and Computer Engineering. His current research interests are in the area of network optimization, cloud computing, and wireless networks.

Dr. Shami is an Editor for IEEE Communications Tutorials and Survey and has served on the editorial board of IEEE Communications Letters (2008-2013). Dr. Shami has chaired key symposia for IEEE GLOBECOM, IEEE ICC, IEEE ICNC, and ICCIT. He has received three best paper awards at leading international conferences. Dr. Shami is the Chair of the IEEE Communications Society Technical Committee on Communications Software. Dr. Shami is a Senior Member of IEEE