At Potsdam University, my group will offer the following courses in the winter term (Oct 13 – Feb 6), possibly with a hybrid option (send an email to [firstname.lastname] [at] uni-potsdam.de if you are interested).
Intro to Causal Inference (full-term lecture series)
Each Monday from 12:15 to 13:45 (Berlin time).
Content: Introduction to causal inference covering basic concepts from causal effect estimation to causal discovery algorithms.
Advanced Causal Inference (full-term lecture series)
Each Monday from 10:15 to 11:45 (Berlin time).
Content: Advanced topics of causal inference (statistics of conditional independence testing, hidden confounders, counterfactuals, mediation, non-stationarity, cycles, high-dimensionality, multiple datasets, representation learning).
Applied Causal Inference (4 sessions online, followed by in-person project phase)
4 sessions on Thursdays from 10:15 to 13:45 (Berlin time) in the first part of the winter term (Oct 16, Oct 23, Oct 30, Nov 6).
Content: Short introduction to causal inference adapted for applied sciences (focus on Earth sciences and a bit on ecology and cognitive science). The following in-person project phase is only for UP students.
From Machine Learning Theory to Practice on a Physical Testbed (full-term seminar)
Each Monday from 16:15 to 17:45 (Berlin time).
Content: In this seminar, students explore the mathematical foundations of modern machine learning through hands-on projects using a physical experimental testbed. Topics include regression, conformal prediction, causal inference, and time series analysis, with a focus on the challenges of applying theory to real-world data. Students work on several mini-projects and present one for grading.
