Bergische Universität Wuppertal


Prof. Dr. Matthias Ehrhardt Prof. Dr. Michael Günther PD Dr. Jörg Kienitz 
Schedule
(Start of Lecture ??.04.20)
Adressen und Termine
(Beginn der VL 07.04., Beginn der UE 12.04)
Vorlesung  Mo, 10:15  11:45  Hörsaal ?? 
Mo, 12:15  13:45  Hörsaal ?? 
The lecture is suitable for students of mathematics as well as for economics.
The students of economathematics can use it as component AKap.NAaAa
"Selected Topics in Numerical Analysis and Algorithms"
in the module of the same name.
Topics of the Lecture:
The aim of the lecture is to introduce the mathematical concepts underlying the
theory of approximating a function using machine learning, resp.
to be more precise deep learning methods.
To this end we give an introduction to the theory and show how this can
be used for exploring Neural Networks.
To illustrate the applicability of Neural Networks in Finance we consider
the pricing of derivatives and the calibration of parametric models.
For pricing we consider the BlackScholesMerton, the HullWhite and the
Heston model. Calibration is considered for the BlackScholesMerton and the
Heston model.
Outline of the lecture:
Literature:
Previous knowledge:
Analysis I  III, basic knowledge of ordinary differential equations, stochastics.
Exercises:
For the exercises we recommend to install the Python
distribution from Anaconda and install the packages tensorflow, keras, pytorch, matplotlib.
If other packages are necessary these can easily installed on demand.
Sheets.
Criteria:
Regular participation and participation in the exercise groups,
as well as reaching 50% of the possible points on the first seven or the remaining
exercise sheets and at least 2/3 of the possible points for the practical tasks.
