Autonomous Learning

Abstract

AUTONOMOUS LEARNING Page 1 in the Sciences Mathematics Max Planck Institute for Autonomous Learning research aims at understanding how autonomous systems can efficiently learn from the interaction with the environment, especially by having an integrated approach to decision making and learning, allowing systems to autonomously decide on actions, representations, hyperparameters and model structures for the purpose of efficient learning. In this summer school international and national experts in this area will introduce to the core concepts and related theory for autonomous learning in real-world environments. The tutorials are structured around three themes: SCIENTIFIC ORGANIZERS ADMINISTRATIVE CONTACT LOCATION Shun-ichi Amari RIKEN, Japan Christos Dimitrakakis Chalmers University of Technology, Sweden Satinder Singh University of Michigan, USA Tamim Asfour KIT Karlsruhe …