Pre-doc Summer School on Learning Systems

About

One of the most prominent scientific challenges of our time is to cope with the complexity which arises in biology, medicine, engineering, economics, sociology and many other areas of high societal relevance. Learning systems are able to perceive large and complex information, and they can adjust and adapt their behavior to influences from their environment.

Natural as well as artificial learning systems are often influenced by highly unreliable, stochastic factors. Both the natural sciences and the engineering sciences with their complementary scientific methods of analysis and synthesis explore such learning systems by interacting with them, by modeling them, and by explicit construction or reconstruction.

Course leader

Jeannette Bohg (Max Planck Institute for Intelligent Systems, Tübingen) Andreas Geiger (Max Planck Institute for Intelligent Systems, Tübingen) Thomas Hofmann (ETH Zürich) Martin Jaggi (EPFL Lausanne) Lubor Ladicky (ETH Zürich) Pawan Kumar (Universit

Target group

Students who are enrolled in a Master’s program in computer science, computer engineering, or a closely related discipline (e.g. electrical engineering, mathematics, physics) or who have finished their master’s degree not more than one year ago, i.e. not earlier than July 2016.

Course aim

The Pre-doc Summer School on Learning Systems aims at teaching a fundamental understanding of perception, learning and adaption in complex systems to master students and to attract them for potential future PhD studies in this field. Students in computer science and related fields get the opportunity to attend tutorial lectures and practicals on various topics related to Learning Systems. In particular, researchers from the Max Planck Institute for Intelligent Systems and ETH Zurich as well as some external lecturers will present fundamental and advanced topics in areas such as causal models, deep learning, learning theory, robotics & control and computer vision.