Experimental Mathematics (MAST90053)
Graduate courseworkPoints: 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.
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 1 |
|---|---|
| Fees | Look up fees |
Modern computers have developed far beyond being great devices for numerical simulations or tedious but straightforward algebra; and in 1990 the first mathematical research paper was published whose sole author was a thinking machine known as Shalosh B Ekhad. This course will discuss some of the great advances made in using computers to purely algorithmically discover (and prove!) nontrivial mathematical theorems in for example Number Theory and Algebraic Combinatorics. Topics include: Automated hypergeometric summation, Groebner basis, Chaos theory, Number guessing, Recurrence relations, BBP formulas.
Intended learning outcomes
After completing this subject, students will:
- have been introduced to non-numerical symbolic computation packages used in modern research in the areas of discrete mathematics and number theory;
- acquire insight into the use of computers for discovering and formally proving mathematical theorems;
- gain the ability to pursue further studies in this and related areas.
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.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
One of the following, or equivalent.
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30028 | Numerical Methods & Scientific Computing | Semester 2 (On Campus - Parkville) |
12.5 |
| MAST30005 | Algebra | Semester 1 (On Campus - Parkville) |
12.5 |
| MAST30012 | Discrete Mathematics | Semester 2 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
It is recommended that students have completed the following, or a similar subject.
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30028 | Numerical Methods & Scientific Computing | Semester 2 (On Campus - Parkville) |
12.5 |
Inherent requirements (core participation requirements)
Students will be expected to carry out computational experiments using symbolic packages.
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 |
|---|---|---|
Up to 40 pages of written assignments (two assignments worth 15% each, due mid and late in semester)
| Second half of the teaching period | 30% |
A take home exam | During the examination period | 70% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Alexandru Ghitza Mode of delivery On Campus (Parkville) Contact hours 36 total, comprising one one-hour lecture and one two-hour practical class per week. Total time commitment 170 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020 Semester 1 contact information
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Informal specialisation Mathematics and Statistics - 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.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
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
Last updated: 3 November 2022