Handbook home
Numerical Methods & Scientific Computing (MAST30028)
Undergraduate level 3Points: 12.5On Campus (Parkville)
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
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 |
---|---|
Fees | Look up fees |
Most mathematical problems arising from the physical sciences, engineering, life sciences and finance are sufficiently complicated to require computational methods for their solution. This subject introduces students to the process of numerical approximation and computer simulation, applied to simple and commonly encountered stochastic or deterministic models. An emphasis is on the development and implementation of algorithms for the solution of continuous problems including aspects of their efficiency, accuracy and stability. Topics covered will include simple stochastic simulation, direct methods for linear systems, data fitting of linear and nonlinear models, and time-stepping methods for initial value problems.
Intended learning outcomes
On completion of this subject, students should:
- Understand the significance and role of both roundoff error and truncation error in some standard problems in scientific computing;
- Be able to write simple numerical programs that utilize a numerical Problem-Solving Environment such as Matlab or NumPy;
- Appreciate the role of computer simulation, as a third method in science, distinct from theory and experiment
- Understand the distinction between the simulation of stochastic and deterministic models
- Be able to use appropriate numerical techniques when undertaking a mathematical or modelling investigation
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments;
- computer skills: the ability to use mathematical computing packages.
Last updated: 15 February 2024
Eligibility and requirements
Prerequisites
One of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20026 | Real Analysis |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
MAST10009 | Accelerated Mathematics 2 | Semester 2 (On Campus - Parkville) |
12.5 |
Plus one of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10007 | Linear Algebra |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
MAST10022 | Linear Algebra: Advanced | Semester 1 (On Campus - Parkville) |
12.5 |
MAST10008 | Accelerated Mathematics 1 | Semester 1 (On Campus - Parkville) |
12.5 |
Plus one of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP10001 | Foundations of Computing |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
COMP20005 | Engineering Computation |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
PHYC20013 | Laboratory and Computational Physics 2 |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
or other evidence of competence in computer programming.
Other evidence could include passing a relevant Year 12 school subject, or a statement of achievement from a relevant MOOC, or passing a programming competency test administered by another University of Melbourne School.
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: 15 February 2024
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 |
---|---|---|
Two computational assignments, one due mid-semester and one late in semester | During the teaching period | 40% |
One computer laboratory examinations
| During the examination period | 60% |
Last updated: 15 February 2024
Dates & times
- Semester 2
Principal coordinator Hailong Guo Mode of delivery On Campus (Parkville) Contact hours 2 x one hour lectures and 1 x two hour computer laboratory class per week Total time commitment 170 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
Time commitment details
Estimated total time commitment of 170 hours
Last updated: 15 February 2024
Further information
- Texts
Prescribed texts
Recommended texts and other resources
C. Moler, Numerical Computing with Matlab, SIAM, 2004.
- Subject notes
This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Informal specialisation Science-credited subjects - new generation B-SCI Informal specialisation Selective subjects for B-BMED Informal specialisation Applied Mathematics Informal specialisation Applied Mathematics Major Applied Mathematics Informal specialisation Applied Mathematics specialisation - Breadth options
This subject is available as breadth in the following courses:
- Bachelor of Commerce
- Bachelor of Environments
- Bachelor of Fine Arts (Acting)
- Bachelor of Fine Arts (Animation)
- Bachelor of Fine Arts (Dance)
- Bachelor of Fine Arts (Film and Television)
- Bachelor of Fine Arts (Music Theatre)
- Bachelor of Fine Arts (Screenwriting)
- Bachelor of Fine Arts (Theatre)
- Bachelor of Fine Arts (Visual Art)
- Bachelor of Music
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 15 February 2024