Objective

Machine learning methods currently represent some of the most powerful and dynamic developments in the financial sector. This course explores various advanced machine learning techniques and highlights their relationships with conventional statistical methods.

This course also addresses the practical challenges associated with the adoption of machine learning in central banks. It provides a forum for central bankers, regulators and supervisors to discuss strategies for implementing machine learning models, thereby enabling an exchange of knowledge among countries on this increasingly important topic. 

Contents

• Interpretable Machine Learning
• Causal Inference
• Natural Language Processing

Participants will have the opportunity to discuss questions about machine learning methods and their practical implementation using sample codes provided in Python. Participants are expected to make active contributions to the discussions, including sharing their previous experiences, lessons-learned and current challenges related to the implementation of machine learning in their own jurisdiction.

Target group

The course is aimed at data-savvy central bankers, regulators and supervisors in areas such as information technology and statistics, or research departments interested in implementing advanced machine learning methods.

Previous knowledge of data analysis (including linear and logistic regression) and statistical software (including base commands in Python) is required.

Technical requirements

Computer with microphone, camera, speakers or headphones; an up-to-date internet browser; Python installation.