Courses

We design and teach interdisciplinary courses on machine learning (ML) and artificial intelligence (AI). Our programs combine technical insight with societal reflection, encouraging participants to explore both how these systems work and what they mean for our world.

🧭 Our Approach

Our teaching is built on three guiding principles that define every course we lead:

• Accessibility — We make machine learning approachable for participants from diverse academic backgrounds. Concepts are introduced with clarity and intuition, without assuming prior technical knowledge.
• Active Learning — Instead of passive lectures, we focus on interaction, experimentation, and discussion. Participants work with real examples and bring in perspectives from their own fields.
• Societal Perspective — Technical ideas are always connected to broader questions. We explore the ethical, legal, and societal implications of AI alongside its methods.

🛠️ Flexible Formats

We adapt our courses to different academic settings and goals. Depending on the context, this may mean:

• Short Workshops — Compact sessions providing a focused introduction.
• Multi-day Academies — Intensive programs combining technical modules, practical exercises, and interdisciplinary discussions.
• Guest Lectures — Thematic modules enriching existing university or scholarship curricula.

🏔️ Current & Past Editions

• 🇦🇹 Obertauern 2026, Austria: Our upcoming flagship summer school in the Austrian Alps. We explore the paradigm shift from classical machine learning to generative models and autonomous agents. Topics include Transformers, RAG, and the societal implications of increasingly autonomous systems.

• 🇩🇪 Banz Abbey 2025, Germany: Summer academy with participants from the College of Europe.

• 🇸🇮 Ljubljana 2024, Slovenia: Joint program with students from the Max Weber Program, Elite Network of Bavaria.

• 🇩🇪 Koppelsberg 2021, Germany: Interdisciplinary summer academy with participants from across Germany.

• 🇬🇧 Cambridge 2019, UK: St. John’s College, Cambridge, with participants from Cambridge University.

📚 Themes We Explore

The exact content of a course depends on the audience and duration. Common themes include:

• Core ML Concepts — what machine learning is, how it differs from traditional programming, and how models learn from data.
• Methods & Techniques — regression, classification, tree-based models, neural networks, evaluation strategies.
• Practical Data Work — using Python and modern libraries to explore datasets, train models, and visualize results.
• Societal Impact — algorithmic bias and fairness, global regulation, AI in healthcare, misinformation, and the future of work.

Interested in hosting a workshop or learning more about our curriculum? Get in touch.