Time and place
Lecture: Mi, 12-14h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
Content
In this lecture we will study new and highly efficient tools from machine learning which are applied to stochastic problems.
This includes neural SDEs as a generalisation of stochastic differential equations relying on neural networks, transformers as
a versatile tool not only for languages but also for time series, transformers and GANs as generator of time series and
a variety of applications in Finance and insurance such as (robust) deep hedging, signature methods and the application of
reinforcement learning.
Previous
knowledge
The prerequisites are stochastics, for some parts we will require a good understanding of stochastic processes.
A (very) short introduction will be given in the lectures – so for fast learners it would be possible to follow the
lectures even without the courses on stochastic processes.
Usability
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Applied Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)