We offer lectures on pure and applied probability theory as well as foundations of AI
Using abstract measure and integration theory, we model random experiments with probability spaces and random variables and analyze their behavior, especially the behavior of sequences of random variables. In advanced courses we study stochastic processes and their applications to AI, but also to biology and physics.
Core courses offered by our group are
- Stochastics I
- Reinforcement learning
- Stochastic processes
- Markov chains and processes
- Optimisation in AI
Additional advanced seminars on stochastic processes and AI applications complement these foundational courses.
Getting to know the team
If you are a Bachelors student and want to explore our chair, we suggest the following lectures in the program:
- Semester 3, Stochastics 1
Lay the foundation: measure‑theoretic probability and the core tools you’ll use everywhere. - Semester 4, Stochastics 2 and Monte Carlo Methods
Go deeper into limit theorems, dependence, and the probabilistic building blocks of algorithms, and pick Monte Carlo for a hands‑on computational track. - Semester 5, Mathematical Finance I and Functional Analysis
Learn rigorous methods for stochastic modeling in finance and the functional‑analytic tools that underpin modern theory. - Semester 6, Stochastic Processes (and/or Reinforcement Learning or Optimization in AI) and Econometrics
Move to path‑wise models and decision‑making agents — or dive directly into RL — while gaining applied modelling skills in econometrics.