EPM5017 Machine learning for biostatistics
Faculty of Medicine, Nursing and Health Sciences
EPM5017 Machine learning for biostatistics is a level 5, 6-credit-point, postgraduate unit from the Faculty of Medicine, Nursing and Health Sciences, offered in 2026 in Semester 2 at Alfred Hospital. It needs MPH5040 and (EPM5027 or EPM5009).
- Credit points
- 6
- Offered in 2026
- Semester 2
- Alfred Hospital
- Assessment
- No exam
- 2 tasks
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before EPM5017
Prerequisites
Pass these before you enrol.
After EPM5017
No unit lists EPM5017 as a prerequisite in the 2026 handbook.
Enrolment rules
Corequisite: Must be enrolled in course version 3421, 3422, M6025 or M5017.
Overview
Recent years have brought a rapid growth in the amount and complexity of health data captured. Among others, data collected in imaging, genomic, health registries and personal devices call for new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction complement classical statistical tools in the analysis of these data. This unit will cover modern machine learning methods particularly useful for large and complex data. Topics include, classification trees, random forests, model selection, lasso, bootstrapping, cross-validation, generalised additive modelling, and regression splines. The statistical software R package will be used throughout the unit.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Alfred Hospital | Online |
Assessment
- 2 x Theoretical exercisesExerciseThreshold hurdle80%
- 2 x Short exercisesExercise20%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Describe situations where machine learning methods can offer advantages over traditional statistical modelling approaches to data analyses in health applications
- 2
Recognise and explain the differences between the goals of description and prediction
- 3
Determine and implement appropriate machine learning approaches for description and prediction in real-world health applications
- 4
Measure and explain the uncertainty of the results of analyses using machine learning approaches
- 5
Interpret the results of analyses using machine learning in light of the assumptions required, the quality of input data, and the sensitivity to the specific technique implemented
- 6
Critically appraise current literature concerning machine learning applications for classification or prediction in health
- 7
Effectively communicate in language suitable for the scientific community the results of analyses using machine learning methods
Workload and teaching
- Teaching approachOnline learning
Off campus: Twelve hours per week, consisting of (on average) 4 hours per week for reading core material, 4 hours per week completing exercises (manual, computer-based, or on-line), 2 hours per week for on-line communication via discussions, and 2 hours per week for assignment preparation. No residential component is required for this unit.
For full details of the teaching approach to this unit, please refer to Moodle.
Learning resources
Required resources
See Moodle for details about required resources.
Contacts
- Chief Examiners
- Professor Andrew Forbes
Common questions
What are the prerequisites for EPM5017?
You need MPH5040 and (EPM5027 or EPM5009) before you enrol. Enrolment rules also apply.
When is EPM5017 offered?
In 2026, EPM5017 runs in Semester 2 at Alfred Hospital, with an online option.
Does EPM5017 have an exam?
No. EPM5017 has 2 assessment tasks and no exam.