Teaching

Courses as a Freelancer

ZIB Academy 2024 - Causal Inference and Machine Learning @ IPN Kiel

Estimation of causal effects using machine learning methods. It is shown how the double machine learning approach uses powerful machine learning algorithms to model complex relationships in order to achieve a robust estimation of causal effects.

Using ML to find out the “Why”? A Tutorial in Causal Machine Learning @ PyCon DE & PyData 2024 Berlin

Machine learning is mostly used for predicting outcome variables. But in many cases, we are interested in causal questions: Why do customers churn? What is the effect of a price change on sales? How can we optimize personalized marketing campaigns or medical treatments? This tutorial introduces participants to the field of Causal Machine Learning (Causal ML). We will start with a basic motivation of causal analysis and share insights on how to recognize causal questions in data science. We will dive into the basics of Causal ML: Why can’t we simply use of-the-shelf ML methods to answer causal questions? The tutorial will focus on the Double Machine Learning approach and demonstrate the use of Causal ML with the Python library DoubleML (Bach et al., 2022). The general introduction will be complemented by hands-on data examples and interactive discussion and Q&A sessions. The tutorial is a great starting point for participants to discover Causality/Causal ML and start their own causal data science projects. Speaker at the PyCon DE & PyData Berlin 2024

Digital Modelling Core (DMC) @ Finance Academy Novartis AG Basel

The course provides an introduction to statistical modeling, machine learning, and forecasting, with a strong emphasis on business applications. Participants will learn key concepts and techniques to analyze data, build predictive models, and make data-driven forecasts to support business decisions.

Courses at the University of Hamburg

Bachelor Degree

Quantitative Risk Management

Tutorials

The course covers fundamental concepts of quantitative risk management, including decision theory, risk measures, and allocation methods. It explores both linear and nonlinear stochastic dependencies, loss severity and frequency distributions, as well as model fitting and validation in these contexts. Additionally, simulation techniques are introduced. The relevance and applicability of the presented methods and techniques are demonstrated through examples from the field of economics.

2024, 2025

Statistics I

Tutorials

Techniques for describing univariate and bivariate data sets, the linear regression model, price indices, time series models, one-dimensional discrete and continuous random variables, important special discrete and continuous distributions. The importance and applicability of the methods and techniques presented are illustrated by examples from the field of economics.

2023, 2024, 2025

Statistics II

Tutorials

Multidimensional distributions and random variables, sampling; parameter estimation, testing of hypotheses, special test problems, multiple linear regression, stochastic time series models.

2023, 2024

Introduction to Mathematics and Statistics in Economics

Tutorials

This course aims to refresh mathematical knowledge acquired in middle and high school and align the varying levels of prior mathematical understanding among students. It prepares students for the transition to a different teaching style and the more advanced mathematical formalism encountered in higher education. The course also focuses on developing subject-specific study skills and alleviating concerns about the mathematical and statistical demands in economics programs, ultimately aiming to reduce dropout and failure rates.

2023, 2024

Master Degree

Seminar Large Language Models (LLMs)

This seminar covers the basics of large language models and their latest applications. If programming skills are available, students can carry out their own experiments and applications. Finally, connections to causal inference will also be discussed.

2024, 2025

Introduction to Deep Learning

Tutorials

The course aims to deepen knowledge in deep learning by focusing on neural networks and their business applications. Students will develop analytical competencies, including the ability to implement and estimate standard models, independently solve application problems, and understand the limitations of deep learning. Topics covered include the fundamentals of deep learning, machine learning principles, optimization techniques, applications in computer vision and time series, and advanced concepts such as GANs.

2024, 2025

Statistical Programming with Python

Tutorials

The aim of the course is to acquire skills in analysing data with the help of modern statistical methods. Topics include hypothesis testing, regression methods (quantile regression, ridge, …) and classification methods (SVMs, decision trees, …). The course consists of a lecture part (in the morning) and an exercise part (in the afternoon). In the lecture, an introduction to Python is given and statistical methods for data analysis are presented. In the exercises, theoretical and practical tasks relating to the statistical methods from the lecture are worked on using Python.

2024, 2025

Seminar Causal Machine Learning

The course will provide students with tools to conduct modern causal inference using machine learning algorithms with focus on empirical economic problems. State-of-theart approaches for inference on causal and structural parameters such as double machine learning are introduced. Therefore, methods from machine learning, which were developed for prediction purposes are presented and their adaptions to learn causal parameters are discussed. The estimation of causal parameters is performed on many practical examples from economics using R / python.

2024

Machine Learning with Applications in Economics and Business Administration

Tutorials

The course will provide a practical introduction to modern high-dimensional function fitting methods - a.k.a. machine learning ML methods (e.g. Lasso, Boosting, Neural Nets) - for efficient estimation and inference on treatment ef-fects and structural parameters in empirical economic models. Participants will use R to allow them to immediately internalize and use the techniques in their own academic and industry work. All lectures, except the introductory one, will be accompanied by R-code that can be used to reproduce the empirical examples in the lectures. Thus, there will be no gap between theory and practice.

2023

Seminar Business Analytics

Algebraic modelling language, data analysis and mining (structured data), data analysis and mining (unstructured data). Data collection, digital and social media, digital transformation (effects, process), digitalisation is an essential topic in the module, empirical digital data, machine learning, artificial intelligence, practical (near) applications, programming, software: data analysis, software: mathematical/statistical (e.g. Python, R)

2023

Seminar Topics in Deep Learning & Machine Learning

The seminar deals with current research work on deep learning and machine learning. Students will work on selected chapters from textbooks and published studies and present these during the course.

2023