Machine Learning for Biostatistics (MAST90141)
Graduate courseworkPoints: 12.5Online
About this subject
Contact information
Semester 2
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: https://study.unimelb.edu.au/
Overview
| Availability | Semester 2 - Online |
|---|---|
| Fees | Look up fees |
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 existing 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.
Intended learning outcomes
On completion of the subject, students will be able to:
- Describe situations where machine learning methods can offer advantages over traditional statistical modelling approaches to data analyses in health applications;
- Recognise and explain the differences between the goals of description and prediction;
- Determine and implement appropriate machine learning approaches for description and prediction in real-world health applications;
- Measure and explain the uncertainty of the results of analyses using machine learning approaches;
- 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;
- Critically appraise current literature concerning machine learning applications for classification or prediction in health; and
- Effectively communicate in language suitable for the scientific community the results of analyses using machine learning methods.
Generic skills
- Independent problem solving
- Facility with abstract reasoning
- Clarity of written expression
- Sound communication of technical concepts
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
All of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90099 | Advanced Regression | September (Dual-Delivery - Parkville) |
12.5 |
| MAST90102 | Foundations of Regression | July (Dual-Delivery - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Short Essay - short exercises with mathematical derivations, explanation of concepts, data analyses.
| Week 4 | 10% |
Report – determination and application of appropriate methods to example health dataset No. 1, interpreting and presenting results in suitably clear scientific language
| Week 8 | 40% |
Short Essay - critical appraisal of a published paper using machine learning methods
| Week 10 | 10% |
Report – determination and application of appropriate methods to example health dataset No. 2, interpreting and presenting results in suitably clear scientific language
| 2 Weeks after the end of teaching | 40% |
Last updated: 4 March 2025
Dates & times
- Semester 2 - Online
Coordinator John Carlin Mode of delivery Online Contact hours Total time commitment 144 hours Teaching period 22 July 2024 to 20 October 2024 Last self-enrol date 2 August 2024 Census date 2 September 2024 Last date to withdraw without fail 20 September 2024 Assessment period ends 15 November 2024 Semester 2 contact information
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: https://study.unimelb.edu.au/
Time commitment details
The subject is entirely online. Students are expected to (1) make regular posts to online discussions in response to topics and questions posed by the co-ordinator and other students (1 hour per week); (2) participate in online tutorials hosted by the co-ordinator (1 hour per week); and (3) study subject materials and complete assessment tasks (10 hours per week).
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Subject notes
This subject is delivered online via our partners in the Biostatistics Collaboration of Australia (www.bca.edu.au). It is not generally available in the Master of Public Health nor in any program outside the MSPGH.
Last updated: 4 March 2025