Teaching

Procedures for Papers and Theses

In the age of "big data" and "advanced analytics", data processing faces new challenges. Queries become more complex and often involve data mining and machine learning tasks, and the scale of the datasets requires new algorithmic approaches. This course covers the theoretical foundations of modern data processing and analytics.

Many real-world systems may be described as a network of dynamically interacting entities. We interact with each other in a social contact network, over which rumors as well as pathogens can spread; electrical energy is delivered by the power grid; the Internet enables almost instantaneous world-wide interactions; our economies rest upon a complex network of inter-dependencies spanning the globe. This course provides an introduction to such network dynamical systems.

In this software lab, we cover a range of tools to analyse networks and processes on networks such as epidemic spreading. Specifically, we cover network analysis tools including spectral clustering, network embeddings and graph neural networks. You will work in small teams of 2–3 people.

Networks are a widely-used model for a wide range of systems from different disciplines. This seminar mainly covers two perspectives: First, networks can be seen as relational data, with tasks related to characterization of networks and individual nodes. Second, we can look at networks as dynamical systems and model their behavior, for example in epidemic spreading.