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VERSION:2.0
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DTSTAMP:20260925T024743Z
DTSTART;VALUE=DATE:20270214
DTEND;VALUE=DATE:20270216
SUMMARY:Machine Learning Methods
TRANSP:TRANSPARENT
UID:2027_15_29_machine_learning_methods_1007626
DESCRIPTION:Objective\n\nMachine learning methods currently rank among th
 e most powerful and rapidly evolving developments in the financial secto
 r. This course explores various advanced machine learning techniques and
  highlights their relationship with conventional statistical methods.\n\
 nThis course also addresses the practical challenges associated with the
  adoption of machine learning by central banks. It provides a forum for 
 central bankers\, regulators\, and supervisors to discuss strategies for
  implementing machine learning models\, thereby facilitating the exchang
 e of knowledge across countries on this increasingly important topic. \n
 \nContents\nInterpretable Machine Learning\nCausal Inference\nNatural La
 nguage Processing\n\nParticipants will have the opportunity to discuss q
 uestions about machine learning methods and their practical implementati
 on using provided sample codes in Python. Participants are expected to m
 ake active contributions to the discussions\, including sharing their pr
 evious experiences\, lessons-learned\, and current challenges related to
  the implementation of machine learning in their own jurisdiction.\n\nTa
 rget group\n\nThe course is aimed at data-savvy central bankers\, regula
 tors and supervisors in areas such as information technology and statist
 ics\, or research departments interested in implementing advanced machin
 e learning methods.\n\nPrevious knowledge of data analysis (including li
 near and logistic regression) and statistical software (including base c
 ommands in Python) is required.\n\nTechnical requirements\n\nComputer wi
 th microphone\, camera\, speakers or headphones\; an up-to-date internet
  browser\; working Python installation.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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