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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 2 weeks before the first day of teaching for the relevant session.
- Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney
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