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 2023 in Semester 2 at Alfred Hospital. It has no prerequisites.
- Credit points
- 6
- Offered in 2023
- Semester 2
- Alfred Hospital
- Assessment
- No exam
- 2 tasks
This is the 2023 handbook entry. See the 2027 entry.
Reviews
No reviews yetNo reviews yet. Be the first to review EPM5017.
Requisites
Before EPM5017
No prerequisites or corequisites besides the enrolment rules below.
After EPM5017
No unit lists EPM5017 as a prerequisite in the 2023 handbook.
Enrolment rules
Pre-requisite:
MPH5040 Epidemiology
EPM5002 Mathematical Background for Biostatistics (or equivalent, e.g. university-level calculus)
EPM5014 Probability and Distribution Theory (or equivalent)
MPH5200 Regression modelling in epidemiology (or equivalent multivariable regression modelling unit)
Students must be enrolled in course 3422, M6025, 3421 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 2023
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Alfred Hospital | De |
Assessment
- 2 x Theoretical exercisesThreshold hurdle80%
- 2 x Short exercises20%
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?
EPM5017 has no prerequisites, but enrolment rules apply.
When is EPM5017 offered?
In 2023, EPM5017 runs in Semester 2 at Alfred Hospital.
Does EPM5017 have an exam?
No. EPM5017 has 2 assessment tasks and no exam.