Handbook home
Machine Learning for Biostatistics (MAST90141)
Graduate courseworkPoints: 12.5Online
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(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: MSPGH Website
- Email: Enquiry Form
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
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: 3 November 2022
Eligibility and requirements
Prerequisites
Students must have completed the below subject prior to enrolment in this subject:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
No longer available |
Students must have completed or be currently enrolled in the below subject prior to enrolment in this subject:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
POPH90121 | Categorical Data & GLMs | No longer available |
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: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
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: 3 November 2022
Dates & times
- Semester 2 - Online
Coordinator Lyle Gurrin Mode of delivery Online Contact hours Total time commitment 144 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 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: MSPGH Website
- Email: Enquiry Form
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 an complete assessment tasks (10 hours per week).
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
Last updated: 3 November 2022