The Summer Course will consist of a two modules described below. Trainees are requested to bring their own laptop with R installed.

Dates: 7-9 July 2024
Venue: CUT Tassos Papadopoulos Building, Cyprus University of Technology, Limassol, Cyprus.
Room: Lecture Room 2, Floor 1.
Speakers: Andreas Artemiou, University of Limassol, Cyprus and Eftychia Solea, Queen Mary University of London, UK.
Christina Erlwein-Sayer, HTW Berlin, Germany.

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.

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 – 16:00 Session 2.3 - Module II
• 16:00 – 16:30 Coffee break
• 16:30 – 18:30 Session 2.4 - Module II