Teaching

Crash Course LLMs for Social Scientists

This workshop provides an introduction to fundamentals of NLP and LLMs for social scientists. It covers basic and advanced text representation, fundamentals of machine learning, transformer architectures, and applied questions regarding LLMs. The course consists of twelve 90-minute sessions, mostly divided into a lecture with a conceptual focus, and a tutorial covering implementation in python.

Materials and Syllabus

Supervised Machine Learning with Imbalanced Data

Self-designed workshop introducing different techniques to deal with imbalanced data in supervised classification problems.

Github Slides

CompText 2022 & 2023

Methods II: Quantitative Methods

Tutorial introducing Master’s students to fundamental concepts of quantitative methods, such as experiments, regression, causality, and hypothesis testing, and their application in R.

Fall 2023, 2024, and 2025, ETH Zurich

Intro to R and Statistics

Tutorial for Master students, focusing on the R programming language.

Fall 2020 and 2021 (two labs each term), Hertie School, Berlin