Topological data analysis and machine learning theory
The Banff International Research Station will host the "Topological data analysis and machine learning theory" workshop from October 14th to October 19th, 2012.
In the last two decades, the dropping cost of acquisition and the sharing ease of the raw data have created a demand for the analysis of its ever-increasing volumes. Examples of situations where this occurs range from protein docking to social networks. Statistics and machine learning traditionally supply methods for such analysis. An emerging field of applied and computational topology has been adapting the ideas of algebraic topology to the requirements of working with noisy real-world data, using gadgets like persistent homology to quantify and visualize important topological features of the data. The goal of this workshop will be to provide a forum for the exchange of ideas between researchers working in various related areas of mathematics, as well as the transfer of knowledge between theory and concrete applications.
The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).
BIRS Scientific Director, Nassif Ghoussoub