The 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.
Learning objective
Prerequisites / Notice
Bachelor in mathematics, physics, economics or computer science.
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course