Machine learning for Science and Society - and Danish Industry - Alsion

English text see below

Dette foredrag diskuterer perspektiverne i kunstig intelligens for videnskaben, samfundet og industrien. Disse bliver illustreret gennem et bredt spektrum af de nyeste anvendelser og landvindinger på området. Fra” kernel-based” metoder til ”deep learning”.

Foredraget er på Engelsk.

The talk discusses the perspectives of machine learning for science, society, and industry by means of a diverse selection of recent applications of state-of-the-art machine learning, ranging from kernel-based methods to deep learning using neural networks.

About Christian Igel

Christian Igel is a professor at the Department of Computer Science at the University of Copenhagen (DIKU). He studied Computer Science at the Technical University of Dortmund, Germany. In 2002, he received his Doctoral degree from the Faculty of Technology, Bielefeld University, Germany, and in 2010 his Habilitation degree from the Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany. From 2003 to 2010, he was a Juniorprofessor for Optimization of Adaptive Systems at the Institut für Neuroinformatik, Ruhr-University Bochum. In October 2010, he was appointed professor with special duties in machine learning at DIKU. Since December 2014 he is a full professor at DIKU. He was an Area Chair of Advances in Neural Processing Systems (NIPS) and serves as an Editor of the German Journal on Artificial Intelligence (KI) and an Associate Editor of the Evolutionary Computation Journal (ECJ) and the Artificial Intelligence Journal (AIJ). Christian Igel is constituent CEO of the SCIENCE AI Centre, an umbrella organization that fosters collaboration within the faculty of science at the University of Copenhagen in research areas related to AI and data science. Currently, Christian Igel’s main research interest are support vector machines and other kernel-based methods, evolution strategies for single- and multi-objective optimization and reinforcement learning, PAC-Bayesian analysis of ensemble methods, and deep neural networks and stochastic neural networks, as well as applications of these methods