Machine Learning

The course exposes participants to recent machine learning (ML) developments and discusses their ramifications for business and economics. ML comprises theories, concepts, and algorithms to extract patterns from observational data. The prevalence of data ("big data") has led to a surge in the interest in ML to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers' mindsets and involve ML to some extent. Familiarizing course participants with these concepts and enabling them to apply cutting-edge ML algorithms to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets Ph.D. students with a general interest in algorithmic decision-making and/or concrete plans to employ ML in their research. A clear and approachable explanation of relevant methodologies and recent ML developments paired with a batterie of practical exercises using contemporary software libraries for (deep) ML will prepare participants for design-science or empirical-quantitative research projects.
 
The course will be offered over a four-day period comprising lecture, tutorial, and discussion sessions.


Registration Deadline: February 1, 2026