Autonomous Learning: Summer School 2014
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 will introduce to the core concepts and related theory for autonomous learning in real-world environments. We hope to foster the enthusiasm of young researchers for this exciting research area, giving them the opportunity to meet leading experts in the field and similarly interested students. Our school offers an opportunity to look into fundamental and advanced aspects of autonomous learning. The tutorials are structured around three themes:
- learning representations,
- acting to learn (exploration), and
- learning to act in real-world environments.
The themes include but are not restricted to the following subjects:
Theme 1: Learning representations
- compressed sensing/sparse coding
- deep learning
- hierarchical representations in perception
- learning abstractions and symbols
Theme 2: Acting to learn (exploration)
- foundations of optimal exploration and information seeking
- approaches to intrinsic motivation
Theme 3: Learning to act in real-world environments
- relational reinforcement learning
- Bayesian cognitive robotics
Financial support
The summer school is financially supported by the DFG Priority Program Autonomous Learning.