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This 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; regularization and shrinkage approaches such as Lasso; tree-based methods including decision tree and random forest. The unit focuses on the applications of statistical machine learning in economics, and computer software such as Python is used throughout the unit.
Study level | Undergraduate |
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Academic unit | Economics |
Credit points | 6 |
Prerequisites:
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(ECMT2150 or ECMT2950) and ECMT2160 |
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Corequisites:
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None |
Prohibitions:
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QBUS3820 |
Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
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The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
Session | MoA ? | Location | Outline ? |
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Semester 2 2025
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Normal day | Camperdown / Darlington, Sydney |
Outline unavailable
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