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Machine Learning for Biostatistics (MAST90141)
Graduate courseworkPoints: 12.5Online
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(opens in new window)
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: 31 January 2024