Seminars on Reinforcement Learning and Complex Networks

Next semester we will offer two seminars!

Exploring Complex Networks

Many real-world phenomena like rumor spreading, contagions in financial markets, epidemic outbreaks, or cognitive processes can be expressed in terms of complex networks. The seminar explores dynamical processes which are linked to networked structures in intriguing ways. In particular, we aim at understanding the complex interplay between the topology and the emerging dynamics of a network. We analyze brain networks or online social networks and explore various techniques for their analysis and classification ranging from diffusion models and stochastic simulations to deep learning approaches.

More information can be found on the course page.

Reinforcement Learning

Reinforcement Learning is — besides Supervised and Unsupervised Learning — one of the outstanding variants of Machine Learning. It has evolved to an effective tool, being able to create AI systems that are able to beat humans in almost every game, e.g. the famous strategy board game Go.

This seminar will try to do both: teach the basics of Reinforcement Learning in theory and also provide the ability to apply it. We will therefore first have a look at the actual state-of-the-art algorithms of Reinforcement Learning and afterwards apply these to the well-known game Connect 4.

More information can be found on the course page.