Statistical Learning

Theme

Invited Speakers

Programme

Abstracts

Organisers

Registration

Assistance

Travel to the Symposium

Accommodation in Sydney

Venue and Parking

Dinner

22 September 2003

University of New South Wales,  Sydney,  Australia

2 - 3 October 2003

Theme

Statistical Learning is one of the most vibrant current areas of statistical research.  In Statistics parlance, its main themes are prediction and classification ("supervised learning") and clustering ("unsupervised learning").  Many of these themes are present in areas outside of mainstream statistics: pattern recognition, artificial intelligence, machine learning, neural networks, data mining and bioinformatics.

This symposium will primarily focus on statistical issues, although some cross-disciplinary research will be presented.

Invited Speakers

• David Bowtell (University of Melbourne)
• Markus Hegland (Australian National University)
• Robert Kohn (University of New South Wales)
• Inna Kolyshkina (PricewaterhouseCoopers)
• Steve Marron (University of North Carolina)
• Rob McCulloch (University of Chicago)
• Brian Ripley (Oxford University)
• Xiaotong Shen (Ohio State University)
• Alex Smola (Australian National University)
• Arcot Sowmya (University of New South Wales)
• Ross Taplin (Murdoch University)
• Matt Wand (University of New South Wales)

Organisers

Inge Koch and Matt Wand.

School of Mathematics, UNSW

The organisers gratefully acknowledge
financial support from AMSI; and
financial support for Professor Ripley from the School of Mathematics, UNSW