Teaching Portfolio

Introduction to Data Science

Digital technologies provide us with an ever-increasing amount of data, e.g. in the context of eCommerce, financial services, digital humanities, computational social science, or life sciences. But how can we extract knowledge from massive volumes of potentially incomplete, time-stamped, and high-dimensional data? How can we reason about relationships and patterns? Can we use those patterns to make predictions that can inform decision-making or help to design recommender systems? And how can we automatically identify relevant features in high-dimensional data?

This course equips students with theoretical and practical skills in data science, data analytics, and statistical learning that can be used to address these questions. It combines theory lectures on key machine learning algorithms with hands-on practice lectures, in which we implement and apply those techniques in python. Throughout the course, we will develop our own library of data science and machine learning techniques from scratch and then explore equivalent functions in data science packages like sklearn or statsmodels. For detailed information please check the syllabus.


Machine Learning for Complex Networks

Graph representations of relational data have become an important foundation to address data science and machine learning tasks across the sciences. Graph mining and learning techniques help us to detect functional modules in biological networks and communities in social networks, to find missing links in social networks, or to address node-, link-, or graph-level classification tasks.

This course covers techniques to address supervised and unsupervised learning tasks in data on complex networks. We show how statistical learning techniques can be used to infer cluster patterns or predict links, introduce methods to learn low-dimensional vector-space representations of graph-structured data, and discuss applications of deep learning to complex networks. The course combines a series of lectures - which introduce theoretical concepts in statistical learning, representation learning, or graph neural networks - with practice sessions that show how we can apply them in practical graph learning tasks. The course material consists of annotated slides for lectures and a series of accompanying jupyter notebooks. For detailed information please check the syllabus.


Statistical Network Analysis

Networks matter! This holds for technical infrastructures like the Internet, for information systems and social media in the World Wide Web, but also for various social, economic and biological systems. What can we learn from the topology of such complex networked systems? What is the role of individual nodes and how can we discover significant patterns in the global structure of networks? How do these structures influence dynamical process? Which are the most influential actors in a social network? And how can we analyse time series data on networks with dynamic topologies?

In this course, students get an introduction to statistical modeling and analysis techniques that can be used to study networked systems across disciplines. The course will show how we can represent networks mathematically and we can characterize patterns in their topology quantitatively. Students will understand how networks shape dynamical processes and how complex link topologies emerge from simple network formation processes. The accompanying practice lectures implement key network analysis and machine learning techniques ans show how network science challenges can be solved using python. For detailed information please check the syllabus.


Current Master & Bachelor Courses

  • BSc Lecture: Algorithms for AI and Data Science II, 24 lectures and exercises (4+2 SWS), Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2023
  • MSc Lecture: Introduction to Computer Science for Jurists, 12 lectures and exercises (2+2 SWS), Faculty of Law, Julius-Maximilians-Universität Würzburg, Germany, winter semester 2022/2023
  • MSc Lecture: Statistical Network Analysis, 12 lectures and exercises (2+2 SWS), Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, winter semester 2022/2023
  • MSc Lecture: Machine Learning for Complex Networks, 12 lectures and exercises (2+2 SWS), Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022
  • BSc/MSc Seminar: Data, AI, and Society, 2 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022
  • MSc Seminar: Social Network Analysis, 2 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022
  • BSc/MSc Seminar: Machine Learning for Complex Networks, 2 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022
  • MSc Lab: Computational Astrophotography, 6 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, winter semester 2022
  • MSc Lab: Graph Neural Networks, 6 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022
  • MSc Lab: Statistical Network Analysis, 6 SWS, Faculty of Mathematics and Computer Science, Julius-Maximilians-Universität Würzburg, Germany, summer semester 2022

Tutorials, PhD Courses & Summer Schools

Past MSc & BSc Courses