Dates: 7-9 July 2025
Venue: Continental Building, Cyprus University of Technology, 115 Spyrou Araouzou Street, 3036 Limassol, Cyprus.
Room: SOED Lecture Hall, Ground Floor.
Speakers: Andreas Artemiou, University of Limassol, Cyprus and Eftychia Solea, Queen Mary University of London, UK.
Christina Erlwein-Sayer and Alla Petukhina, HTW Berlin, Germany.
The coffee breaks on the 7th of July will take place in the departmental room on the 2nd floor of the Continental Building. The coffee breaks on the 8th and the 9th of July will take place in the foyer on the ground floor of CUT Tassos Papadopoulos Building, Athinon, Limassol 3041, Cyprus, 200 meters away.
To access the tutorial online, click here. Accessing this conference implies accepting the conditions. The password will be shown in the registration tool only to registered participants. Attendance is restricted to participants registered for the tutorial only. When joining on Zoom, please ensure to use your name as registered for the conference. Non-registered users will be promptly removed by the conference staff.
Sufficient dimension reduction in supervised settings
Andreas Artemiou, University of Limassol, Cyprus.
Eftychia Solea, Queen Mary University of London, UK.
Description: An introduction to Sufficient Dimension Reduction (SDR) will be provided. SDR is a supervised dimension reduction framework which allows for linear and nonlinear feature extraction. We will start by introducing some general concepts, and we will then discuss the classic methodology which uses inverse moments. This approach has seen methodological and computational advances in a number of different directions, which will be discuss in this course. We will also share code in R which performs SDR. At the end of the course, the students will be able to understand the theoretical background and be able to use a wide variety of methodologies in the SDR framework.
Time Series Modelling with ML and (explainable) AI in Finance
Christina Erlwein-Sayer, HTW Berlin, Germany.
Alla Petukhina, HTW Berlin, Germany.
Description: This module focuses on time series modelling with Machine Learning (ML) methods and explainable AI (XAI) in the financial sector with a particular focus on the evaluation of AI models. The participants will work on ML methods for time series analysis and explore model-agnostic tools to assess predictions. The methods aim to bridge the gap between predictive forecast models and their applications, focusing on assessment of robustness and accuracy. Post-processing techniques such as Shapley Values and feature importance will be discovered to gain model explainability. The workshop will highlight the integration of XAI in time series analysis for financial data, and the assessment of AI models.
Topics include:
a) Introduction to ML and Explainable AI (XAI).
b) Fundamental Concepts in Statistical Learning: Regression, Classification, and Clustering.
c) XAI Techniques in Statistical Learning.
d) ML and XAI in Time Series Analysis.
e) Case Studies in Python in XAI Applications: Credit Risk and Asset Management.
Monday, 7 July 2025
- 09:00 – 10:30 Session 1.1 - Module I
- 10:30 – 11:00 Coffee break
- 11:00 – 12:30 Session 1.2 - Module I
- 12:30 – 14:00 Lunch break
- 14:00 – 15:30 Session 1.3 - Module I
- 15:30 – 16:00 Coffee break
- 16:00 – 17:30 Session 1.4 - Module I
Tuesday, 8 July 2025
- 09:00 – 10:30 Session 1.5 - Module I
- 10:30 – 11:00 Coffee break
- 11:00 – 12:30 Session 1.6 - Module I
- 12:30 – 14:00 Lunch break
- 14:00 – 15:30 Session 1.7 - Module I
- 15:30 – 16:00 Coffee break
- 16:00 – 17:30 Session 1.8 - Module I
Wednesday, 9 July 2025
- 09:00 – 10:30 Session 2.1 - Module II
- 10:30 – 11:00 Coffee break
- 11:00 – 12:30 Session 2.2 - Module II
- 12:30 – 14:00 Lunch break
- 14:00 – 15:30 Session 2.3 - Module II
- 15:30 – 16:00 Coffee break
- 16:00 – 17:30 Session 2.4 - Module II