This course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep networks and wavelet analysis, Deep Hedging, Deep calibration, Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversarial networks, Economic games, Large Language Models in Finance.
Bachelor in mathematics, physics, economics or computer science.
Lectures take place on Wed 10:15-13:00 at HG G 3 .
Lectures and classes will not take place during Easter week from Friday, 18.04.2025 to Sunday, 27.04.2025.
Lecture notes are provided as ipython notebooks or in form of slides as well as of classical notes.
The assistants of Group 3 (Probability Theory, Insurance Mathematics and Stochastic Finance) offer regular office hours for questions on courses and exercise classes taught by the professors in the group.
During the semester, the assistant hours take place Mondays and Thursdays, 12:00–13:00, in room HG G 32.6. The regular assistant hours start in the fourth week of the semester. Click here for more information.
Exercises will be available in the exercise class. Students are expected to voluntarily do calculations and present results in class. Solutions will also be released right during the exercise class.
Exercise classes take place on Mon 13:15-14:00 at HG D 3.2.