MTH3310 - Applied mathematical modelling - 2019

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Science

Organisational Unit

School of Mathematical Sciences

Chief examiner(s)

Professor Andreas Ernst

Coordinator(s)

Professor Andreas Ernst

Unit guides

Offered

Clayton

  • Second semester 2019 (On-campus)

Prerequisites

Students must be enrolled in the Master of Financial Mathematics or have passed one of the following units: MTH2032, MTH2040 and one of MTH2021, MTH2025

Synopsis

This unit will broaden students' exposure to the toolkit of applied mathematics techniques required to tackle various problems encountered in real-world modelling. Building on the prerequisite knowledge of linear algebra and multivariable calculus, students will learn methods for reducing real world problems into mathematical ones. The students will then learn a broad range of analytical techniques focused on some commonly encountered systems in modelling. Some of these topics include solving optimization problems, fitting models to data, analysing dynamical systems and partial differential equation models. Application areas include traffic modelling, image processing, inventory management, logistics and other scientific and industrial problems. Students will have the opportunity to consider a specific real world problem using their own mathematical modelling approach alongside a client/supervisor. Assessment will include working in teams to solve real-world problems, and presenting the results to the client.

Outcomes

On completion of this unit students will be able to:

  1. Understand specific basic knowledge and display key technical skills in optimisation, model fitting, dynamical systems, and partial differential equations, and their applications;
  2. Develop, apply, integrate and generate knowledge through abstraction and by using high-level critical thinking skills to analyse and solve mathematical problems;
  3. Apply knowledge of mathematics and sound mathematical modelling to a range of applications across science, medicine, economics or engineering;
  4. Collect, organise, analyse and interpret quantitative information meaningfully, using mathematical and/or statistical tools as appropriate to the sub-discipline of specialisation;
  5. Demonstrate skills in the written and oral presentation of a mathematical argument that enable mathematical concepts, processes and results to be communicated effectively to diverse audiences;
  6. Work both individually and collectively with staff and colleagues on the synthesis of mathematical knowledge and the application of mathematical skills to problem solving.

Assessment

End of semester examination (3 hours): 60% (Hurdle)

Continuous assessment: 40% (Hurdle)

Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for both the end-of-semester examination and continuous assessment components.

Workload requirements

One 1-hour lecture, one 2-hour lecture and one 2-hour applied class per week

See also Unit timetable information

This unit applies to the following area(s) of study