Units / MMA3001
MMA3001 · Numerical methods and machine learning
2026 Handbook6 credit pointsLevel 3Department of Mechanical and Aerospace Engineering
Last checked: 23 Aug 2026 UTCOverview
This unit conveys the fundamentals of numerical analysis techniques and their application to data analysis and the solution of engineering problems. You will be introduced to programming structures, use of AI in programing, software documentation, version control and data management, profiling and hardware optimisation, conventions of scientific computing, numerical errors and stability, techniques for interpolation, integration, the solution of ordinary and partial differential equations, and principles of machine learning.
Areas of study: E3001 Bachelor of Engineering (Honours) - Specialisation: Aerospace engineering E3001 Bachelor of Engineering (Honours) - Specialisation: Mechanical engineering
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | Second semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Malaysia | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | In-class test 1 | Quiz / Test | 25% | — |
| 2 | In-class test 2 | Quiz / Test | 25% | — |
| 3 | In-class test 3 | Quiz / Test | 25% | — |
| 4 | Project submission | Project | 25% | — |
| 5 | Life-long learning demonstration | Written | 0% | Competency |
Continuous assessment: 100% Assessment in this unit includes competency hurdle assessment task/s. The consequence of not achieving a competency hurdle is a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
- ENG2005 — Advanced engineering mathematics
Learning outcomes
- Assess the suitability and limitations of machine learning for current and emerging engineering applications, demonstrating awareness of future directions in engineering practice.
- Demonstrate effective use of numerical analysis and machine learning tools to develop defensible solutions to open-ended engineering problems.
- Apply appropriate mathematical and numerical techniques to solve common engineering problems, and evaluate program performance, error, stability, and accuracy.
Workload
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
| Activity | Duration |
|---|---|
| Workshops | 24 hours |
| Practical activities | 24 hours |
Ask about MMA3001
Answered from the Handbook fields above — no AI, no guessing. Every answer links back to the source.
Community discussions about MMA3001
CommunityStudent experience, not official rules. Nothing here changes what the Handbook says.