I teach courses at the master’s level within the Faculty of Business and Economics at the University of Antwerp. My teaching focuses on practical and applied data science, equipping students with the skills to build, evaluate, and deploy machine learning systems responsibly.
Courses
Data Engineering
Master · Business Engineering & EconomicsAn introduction to the infrastructure side of data science: building reliable data pipelines, working with databases and cloud storage, and preparing data at scale. Students learn to design end-to-end data workflows using modern tooling such as SQL, NoSQL databases, and orchestration frameworks.
Machine Learning for Business
Master · Business Engineering & EconomicsA practical course covering the core machine learning methods most relevant to business applications: supervised and unsupervised learning, model selection, evaluation, and interpretation. Emphasis is placed on applying these techniques to real-world business problems and understanding their limitations.
Deep Learning for Business
Master · Business Engineering & EconomicsA hands-on course on deep learning with a business focus. Topics include neural network architectures (convolutional, recurrent, transformer-based), large language models, and generative AI — with emphasis on responsible deployment, interpretability, and real-world case studies.
Python for Machine Learning
Master · Applied Economic SciencesAn introductory course covering the Python ecosystem for data science and machine learning: data manipulation with pandas, data visualisation, and classical machine learning with scikit-learn. Students build practical skills through applied projects on real-world datasets.