Machine Learning in Finance: Its Technology in Perspective
SFI Prof. Josef Teichmann, Professor Stochastic Finance Group, ETH Zürich and member of the ETH Risk Center
Bastian Bergmann, Executive Director of the ETH Risk Center
Currently FULLY BOOKED; registrations possible (waiting list)
Current Situation
The fascinating successes of Machine Learning (ML) in language processing, image recognition or multi-player games have triggered many fantasies to apply these technologies in other fields as well, including the area banking and finance. We are therefore witnessing a tremendous growth in the adoption of ML tools in the financial industry over the last years. According to executives, the impact of the adoption is still mixed. Why is this the case, and which are the perspectives of ML technology in banking and finance?
In order to assess the potential of ML it is crucial for professionals to have both: A subject matter experience of ML techniques as well as a deep understanding of its scope and limitations.
Objective
In this SFI Master Class we will go through some basic concepts of ML and its most common tools and programming techniques used in latest research in banking and finance. At the same time, we will elaborate on the conceptual frameworks -- putting the current approaches into historic context. By opening up the conceptual foundations of AI we aim to elucidate what kind of problem of today we can translate into ML problems and which ones not.
Target Audience
The Master Class is aimed at everybody who wants to deepen her knowledge in machine learning and its potentials in modern financial industry. An interest in the conceptual underlying and in the philosophy of ML or AI is also welcome. Different backgrounds like economics, finance, or quantitative finance are welcome. No programming skills are required but one should not be afraid of discussing code.
SAQ Recertification
This Master Class is an acknowledged SAQ recertification measure for the CWMA and CCoB profile and comprises four learning hours.