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 adversersial networks, Economic games.
Bachelor in mathematics, physics, economics or computer science.
Lecture notes are provided as ipython notebooks or in form of slides as well as of classical notes.
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 Wed 10:15-11:00 at HG E 21 and LEE D 101.