The lecture “Introduction to Stochastic Optimization and Reinforcement Learning” serves as an introduction to analyzing common numerical problems appearing in modern machine learning applications. We will particularly focus on supervised learning and the dynamics of stochastic gradient descent, as well as Reinforcement Learning. This involves the analysis of stochastic processes in discrete time, whose behavior is closely linked to deterministic, as well as stochastic, differential equations. While the general techniques for the asymptotic analysis of stochastic processes are also introduced, proper basic knowledge of probability theory (including martingale theory) is required.
The lecture will be held twice a week in the first half of the semester (Wednesday 14-16, MA144 and Thursday 8-10, MA 751). A corresponding seminar takes place in the second half of the semester. The seminar deals with applications to Stochastic Optimization and Reinforcement Learning in Mathematical Finance.
- Trainer/in: Sebastian Kassing