The goal of this course is to provide you with a quick but solid introduction to a theoretical foundations of machine learning.
WHAT YOU'LL GAIN FROM THE COURSE:
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Become critical consumers of machine learning research, including an understanding when new methods might or might not be useful for empirical work in economics
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Develop your own research agenda around importing ideas from machine learning into economic and econometric theory
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Be able to speak to the machine learning literature, contributing ideas from economics
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Who is the Course for?
The target audience for this course is Graduate Students completing a PhD or MPhil in Economics, and professionals working in central banks and international institutions in research. For a definitive ranking of the mathematical and theoretical skills needed for this course, please view the brochure.
Choose to follow the Econometrics Pathway to deepen your knowledge of Econometrics with courses in Treatment Effects (morning session) and Machine Learning (afternoon session). Or mix and match, choosing different morning or afternoon sessions from our other pathways: Applied Microeconomics or Macroeconomics.
