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Unit of study_

ECMT3185: Econometrics of Machine Learning

The unit introduces the theory and application of statistical machine learning. Topics covered include supervised versus unsupervised learning; regression and classification; resampling methods including cross-validation and Bootstrap; regularisation and shrinkage approaches such as Lasso; tree-based methods including decision tree and random forest; and support vector machines. The unit focuses on the applications of statistical machine learning in economics, and computer software such as R and Matlab are used throughout the unit.

Code ECMT3185
Academic unit Economics
Credit points 6
Prerequisites:
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(ECMT2150 or ECMT2950) and ECMT2160
Corequisites:
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None
Prohibitions:
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QBUS3820

At the completion of this unit, you should be able to:

  • LO1. understand the objective of statistical machine learning
  • LO2. understand different machine learning methods, including basic mathematical derivations
  • LO3. identify applications to which certain machine learning methods can be applied
  • LO4. evaluate advantages and disadvantages of different machine learning methods.

Unit outlines

Unit outlines will be available 1 week before the first day of teaching for the relevant session.