Heidelberg University

Empirical Inference and Learning from Data

Michael Hirsch, Max Planck Institute for Intelligent Systems, Tübingen

Abstract:

Science has always been data-driven. Building models of our world and drawing conclusions from empirical data is at the very heart of any process of knowledge discovery in the natural sciences. Though, what is changing dramatically is the amount of data with which scientists now engage. Major experiments and facilities are now generating petabytes of data per year. The importance of automatic data evaluation and analysis tools for the success of these experiments is undisputed.

However, there seems to exist a gap between developments in modern data analysis and some parts of the physical sciences in which they could find ready use. This course will provide an introduction to the field of empirical inference and machine learning and aims to equip experimental researchers with modern data analysis tools to enable them to tackle extant challenges in physical science.