Teaching

University courses

  • Probabilistic Artificial Intelligence (PAI) — ETH Zürich
    Fall 2021, Fall 2022, Fall 2023, Fall 2025
    Topics: Bayesian modeling, probabilistic inference, uncertainty quantification, variational methods, reinforcement learning.
    Course page · Syllabus · Materials

  • Introduction to Machine Learning (IML) — ETH Zürich
    Spring 2021, Spring 2022, Spring 2023, Spring 2024 (Head TA with more than 1200 students enrolled.), Spring 2025
    Topics: supervised/unsupervised learning, generalization, regularization, linear models, trees, kernels, basics of deep learning.
    Course page · Syllabus · Materials

Student supervision

I was fortunate to supervise the following students:

  • Arnav Sukhija — Time-Adaptive Robotic Control (Bachelor thesis, 2025)
  • Klemens Iten — Scalable and Efficient Exploration via Intrinsic Rewards in Continuous-time Dynamical Systems (Semester project, 2025)
  • Balduin Dettling — Continuous-Time Approximate Dynamic Programming as an Active Learning Problem (Master thesis, 2024)
  • Ivan Rodriguez — Unsupervised reinforcement learning in the real-world (Master thesis, 2023)
  • Jonas Hübotter — Information-based transductive learning (Master thesis, 2023)
  • Hong Chul Nam — Continuous-time reinforcement learning in the real-world (Semester project, 2023)
  • Balduin Dettling — Approximate optimal control (Semester project, 2023)
  • Fredrik Nestaas — Theoretically Motivated Neural ODE Architectures for Stable Adjoint Behavior (Semester project, 2022)
  • Laurens Lueg — Approximate Bayesian Inference for Continual Learning of Dynamical Systems (Master Thesis, 2022)
  • Laurens Lueg — Learning Latent Space Dynamics Models from High-Dimensional Data using Distributional Gradient Matching (Semester thesis, 2022)
  • Cedric Della Casa — Efficient Training of Deep Neural Dynamics Models ( Master thesis, 2021)