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DTSTAMP:20260506T150826Z
DTSTART;VALUE=DATE:20240923
DTEND;VALUE=DATE:20240927
SUMMARY:Introduction to machine learning
TRANSP:TRANSPARENT
UID:2024_05_13_introduction_to_machine_learning_917768
DESCRIPTION:Deadline for application\n\n6 September 2024\n\nObjective\n\n
 Machine learning methods currently represent some of the most powerful a
 nd dynamic developments in the financial sector. This course introduces 
 the building blocks of machine learning and discusses selectedmethods\, 
 making connections between them and conventional statistical methods. Th
 e discussion of each method is followed by a practical session with exam
 ples and exercises in R.\n\nThis course also addresses the practical cha
 llenges associated with the adoption of machine learning. It provides a 
 forum for central bankers\, regulators and supervisors to present and di
 scuss strategies to develop and implement machine learning models\, ther
 eby enabling an exchange of knowledge among countries on this increasing
 ly important topic. \n\nContents\nShrinkage methods\nDecision trees\nEns
 emble methods\nBias-variance trade-off\nAdvantages and limitations of ma
 chine learning methods\nDiscussion of case studies from course participa
 nts\n\nStarting on the second day\, there will be a Q & A slot before ev
 ery session. Participants will have the opportunity to discuss the conte
 nt of the previous day and any challenges they encountered with the prac
 tical exercises which involved independent programming in R.\n\nTarget g
 roup\n\nThe course is aimed at data-savvy central bankers\, regulators a
 nd supervisors in areas such as information technology and statistics\, 
 or research departments interested in implementing machine learning meth
 ods.\n\nFundamental knowledge of data analysis (including linear and log
 istic regression) and statistical software (including commands in R) is 
 required.\n\nParticipants are expected to make an active contribution to
  the discussions and will be invited to present and discuss current chal
 lenges related to the implementation of machine learning in their own ju
 risdiction. There will be opportunities to present independent analyses 
 related to the topic of the course.\n\nTechnical requirements\n\nCompute
 r with microphone\, camera\, speakers or headphones\; an up-to-date inte
 rnet browser.
LOCATION:Online platform
CONTACT:Deutsche Bundesbank – CIC\, tzk@bundesbank.de\, +49 69 9566-36605
 
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