Handbook

MAST30028 Numerical Methods & Scientific Computing

Credit Points: 12.5
Level: 3 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2017:

Semester 2, Parkville - Taught on campus.Show/hide details
Pre-teaching Period Start not applicable
Teaching Period 24-Jul-2017 to 22-Oct-2017
Assessment Period End 17-Nov-2017
Last date to Self-Enrol 04-Aug-2017
Census Date 31-Aug-2017
Last date to Withdraw without fail 22-Sep-2017


Timetable can be viewed here.
For information about these dates, click here.
Time Commitment: Contact Hours: 2 x one hour lectures and 1 x two hour computer laboratory class per week
Total Time Commitment:

Estimated total time commitment of 170 hours

Prerequisites:

One of

Subject
Study Period Commencement:
Credit Points:
Semester 1, Semester 2
12.50

Plus one of:

Subject
Study Period Commencement:
Credit Points:
Summer Term, Semester 1, Semester 2
12.5

Note - In 2018 it is planned to add in the following prerequisite:

Plus one of
COMP10001 Foundations of Computing
COMP20005 Engineering Computation
PHYC20013 Laboratory and Computational Physics 2

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
Recommended Background Knowledge:

Some ability in computer programming is helpful

Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability

Coordinator

Assoc Prof Steven Carnie

Contact

Email: stevenlc@unimelb.edu.au

Subject Overview:

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.

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
Assessment:
  • two computational assignments, one due mid-semester and one late in semester (40%), and
  • one 3-hour computer laboratory examinations, held in the examination period (60%)
Prescribed Texts: None
Recommended Texts:

C. Moler, Numerical Computing with Matlab, SIAM, 2004.

Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
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.
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 Majors/Minors/Specialisations: Applied Mathematics
Applied Mathematics
Applied Mathematics
Applied Mathematics
Applied Mathematics (specialisation of Mathematics and Statistics major)
Science-credited subjects - new generation B-SCI and B-ENG.
Selective subjects for B-BMED

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