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VERSION:2.0
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DTSTAMP:20260915T213504Z
DTSTART;VALUE=DATE:20260301
DTEND;VALUE=DATE:20260303
SUMMARY:Machine learning methods
TRANSP:TRANSPARENT
UID:2026_03_02_machine_learning_881858
DESCRIPTION:Objective\n\nMachine learning methods currently represent som
 e of the most powerful and dynamic developments in the financial sector.
  This course explores various advanced machine learning techniques and h
 ighlights their relationships with conventional statistical methods.\n\n
 This course also addresses the practical challenges associated with the 
 adoption of machine learning in central banks. It provides a forum for c
 entral bankers\, regulators and supervisors to discuss strategies for im
 plementing machine learning models\, thereby enabling an exchange of kno
 wledge among countries on this increasingly important topic. \n\nContent
 s\nInterpretable Machine Learning\nCausal Inference\nNatural Language Pr
 ocessing\n\nParticipants 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 activ
 e contributions to the discussions\, including sharing their previous ex
 periences\, lessons-learned and current challenges related to the implem
 entation of machine learning in their own jurisdiction.\n\nTarget group\
 n\nThe course is aimed at data-savvy central bankers\, regulators and su
 pervisors in areas such as information technology and statistics\, or re
 search departments interested in implementing advanced machine learning 
 methods.\n\nPrevious knowledge of data analysis (including linear and lo
 gistic regression) and statistical software (including base commands in 
 Python) is required.\n\nTechnical requirements\n\nComputer with micropho
 ne\, camera\, speakers or headphones\; an up-to-date internet browser\; 
 Python installation.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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