Machine Learning in Finance
Its technology in perspective.
Block Course | Spring 2021
Full programme: DownloadDownload (PDF, 2.5 MB)
About the course
The fascinating success of Machine Learning (ML) in language processing, image recognition or multi-player games has triggered many fantasies. One of them is to apply these technologies in other fields, such as banking and finance. In the last few years, the adoption of ML tools in the financial industry grew tremendously. According to executives, however, the use of ML tools has yielded mixed results. Why is this the case and what are the perspectives of ML in banking and finance?
This course goes through the basic concepts of ML and the most common tools and programming techniques used in state-of-the-art research. We elaborate on the conceptual frameworks of ML and describe the historical context of current approaches. By opening up the conceptual foundations of AI, we find out which problems translate well into ML problems and which ones don't. Finally, we integrate ML applications from other areas: text mining, modelling extreme events and intelligent maintenance.
Setting: Virtual + Classroom
As we cannot foresee how restrictions due to the COVID-19 pandemic will evolve, the course combines online sessions at first, and physical sessions later if allowed. The latter will foster discussion and networking at ETH Zürich.
The course comprises 7 afternoon sessions (15:00-19:00) on Fridays in Spring 2021.
Programme
Pre-Session (optional) -- Introduction to programming and basic applications: Onboarding with an introduction to Python programming for Machine Learning
Feb 12, 2021 / 15:00-19:00 - online via Zoom
Session 1 - 3: Fundamentals of Machine Learning with recent applications in Finance
Feb 26, 2021 / 15:00-19:00 - online via Zoom
Mar 5, 2021 / 15:00-19:00 - online via Zoom
Mar 19, 2021 / 15:00-19:00 - online via Zoom
Session 4: Further applications in Finance
Apr 9, 2021 / 15:00-19:00 - (Location: tba)
Session 5: Machine Learning applications to other areas
Apr 23, 2021 / 15:00-19:00 - (Location tba)
Session 6: Perspectives from the insurance industry and the regulator
Apr 30, 2021 / 15:00-19:00 - (Location: Swiss Re)
Additional Session with lectureres and networking Apéro
Nov 24, 2021 / 17:30-18:30 - at ETH Zurich
Lecturers
Prof. Dr. Josef Teichmann (Course Director), Stochastic Finance Group, D-MATH
Dr. Bastian Bergmann (Course Director) ETH Risk Center
Prof. Dr. Patrick Cheridito, RiskLab, D-MATH
Prof. Dr. Olga Fink, Intelligent Maintenance, D-BAUG
Dr. Sebastian Becker, Risk Lab, ETH Zurich
Dr. Volker Britz, ETH Risk Center
Associated Lecturers
Jeff Bohn, Swiss Re Institute
Dan Wunderli, FINMA
Language
English.
Number of Participants
The number of participants is limited to 20.
Course Fee and Registration
CHF 2 000.-
Registration is now open!
Who can attend?
Our block courses are designed to appeal to a wide audience of decision-makers. They provide actionable information for all professionals who play a role in managing risks in their organization—not just IT professionals. This course targets individuals who want to deepen their knowledge in machine learning and unlock its potential in the financial industry. An interest in the underlying concepts and in the philosophy of ML or AI is welcome. Different backgrounds like economics, finance, or quantitative finance are welcome. Basic programming skills (Python) are preferable in order to follow the coding examples. However, you can follow the course and leave out the programming part.
Impressions from past students
Good course, even though difficult at times; but then again, it is hard to satisfy all needs and some participants sounded fairly advanced. Even though not trivial, I liked the mathematical background which also makes sense given it is an ETH course.
The course gave me a solid introduction to the topic. I use the course as a starting point for an in-depth examination of machine learning.