Skip to main content
Unit of study_

ECMT3185: Econometrics of Machine Learning

2024 unit information

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.

Unit details and rules

Managing faculty or University school:


Code ECMT3185
Academic unit Economics
Credit points 6
(ECMT2150 or ECMT2950) and ECMT2160
Assumed knowledge:

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 availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

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 ? 
Semester 2 2024
Normal day Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 2 2022
Normal day Camperdown/Darlington, Sydney
Semester 2 2022
Normal day Remote
Semester 2 2023
Normal day Camperdown/Darlington, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.