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Unit of study_
QBUS2820: Machine Learning for Analytics
2027 unit information
This unit introduces the foundational concepts and techniques of machine learning for analytics. Students will learn a structured approach to machine learning: defining a learning problem, selecting a model class, specifying a loss function, fitting models to data, and evaluating their performance on unseen observations. The emphasis is placed on conceptual understanding, practical application, and business relevance rather than on formal mathematical theory. Students will explore core approaches in supervised and unsupervised learning, including regression, classification, and clustering, alongside key principles such as empirical risk minimisation, model validation, and regularisation. By the end of the unit, students will be able to formulate machine learning problems, apply and compare different methods, evaluate model performance appropriately, and interpret results in business contexts. The unit provides a practical grounding in applying machine learning techniques to real-world analytical problems while also preparing students with a strong foundation for more advanced study in machine learning and related areas.
Unit details and rules
Managing faculty or University school:
Business (Business School)
| Study level | Undergraduate |
|---|---|
| Academic unit | Business Analytics |
| Credit points | 6 |
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Prerequisites:
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(BUSS1104 or BUSS1020 or ECMT1010 or ENVX1001 or ENVX1002 or DATA1001 or DATA1901) and (QBUS1040 or MATH1061 or MATH1062) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Foundational knowledge of probability theory, calculus, and linear algebra. |
At the completion of this unit, you should be able to:
- LO1. select and use the appropriate technique to analyse the structure of multivariate data especially when individual data points are identified as belonging to different classes (e.g. failure in credit repayments).
- LO2. apply multivariate data techniques using a training data set to predict classifications for real data
- LO3. understand the characteristics of time-series data in order to analyse real business data of this form
- LO4. select and use an appropriate technique to predict the future behaviour of business variables of interest, including the prediction of discrete outcomes.
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 ? |
|---|---|---|---|
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Semester 1 2026
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Normal day | Camperdown/Darlington, Sydney | |
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Semester 2 2026
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Normal day | Camperdown/Darlington, Sydney |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2027
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Semester 2 2027
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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