Overview

This unit of study aims to provide students with mathematical knowledge and skills needed to support their concurrent and subsequent engineering and science studies.

Requisites

Prerequisites
MTH10012 Calculus and Applications

AND ONE OF:
MTH10013 Linear Algebra and Applications
OR
MTH10003 Engineering Mathematics 2P *
OR
MTH10007 Engineering Mathematics 2 *

Antirequisites
MTH20014 Matrices, Vector Calculus and Complex Analysis
MTH20017 Mathematical Methods and Statistics for Engineering
Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024
Semester 1
Location
Hawthorn
Start and end dates
03-March-2025
01-June-2025
Last self-enrolment date
16-March-2025
Census date
31-March-2025
Last withdraw without fail date
24-April-2025
Results released date
08-July-2025
Semester 2
Location
Hawthorn
Start and end dates
04-August-2025
02-November-2025
Last self-enrolment date
17-August-2025
Census date
31-August-2025
Last withdraw without fail date
19-September-2025
Results released date
09-December-2025

Learning outcomes

Students who successfully complete this unit will be able to:

  • Calculate the eigenvalues and eigenvectors of 2x2 and 3x3 matrices (K2, S1)
  • Interpret the quadratic form and find its canonical form (K2, S1)
  • Execute numerical solutions of initial and boundary value problems (K2, S1)
  • Use finite difference methods to obtain numerical solutions of selected partial differential equations (K2, S1)
  • Demonstrate competence using Matlab to solve statistical and computational mathematics problems (S1)
  • Appropriately apply probability concepts including unconditional and conditional probability, probability distributions, population measures of location and dispersion to analyse data; use concepts in correlation and regression, chi-square test to analyse relationships in bivariate data (K2, S1)
  • Apply the basic concepts of statistical inference including interval estimation, sample size and hypothesis testing in various contexts (K2, S1)

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Lecture		3.00 12 weeks 36
On-campus
Class		1.00 12 weeks 12
On-campus
Class		1.00 12 weeks 12
Online
Directed Online Learning and Independent Learning		1.50 12 weeks 18
Unspecified Activities
Independent Learning		6.00 12 weeks 72
TOTAL150

Sarawak

Type Hours per week Number of weeks Total (number of hours)
On-campus
Lecture		3.00 12 weeks 36
On-campus
Class		1.00 12 weeks 12
On-campus
Class		1.00 12 weeks 12
Unspecified Activities
Independent Learning		7.50 12 weeks 90
TOTAL150

Assessment

Type Task Weighting ULO's
ExaminationIndividual 40 - 55% 1,2,3,4,6,7 
Online AssignmentIndividual 10 - 20% 6,7 
ProjectIndividual/Group 15 - 20% 3,4,5 
Test 1Individual 10 - 15% 1,2 

Hurdle

As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:

(i) an aggregate mark of 50% or more, and(ii) at least 40% in the final exam.Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.

Content

  • Matrix Analysis: eigenvalue problems, reduction to canonical form, science and engineering applications
  • Initial value problems for ordinary differential equations: Taylor series and finite differences; order notation; local error estimation; Euler and Runge-Kutta methods for first order ordinary differential equations; extension to systems of equations and higher order equations
  • Boundary value problems for ordinary differential equations: central difference approximation and finite difference solutions
  • Finite difference solution of selected linear partial differential equations occurring in science and engineering applications
  • Applied Probability and Statistics: exploratory data analysis, probability, random variables and probability distributions, hypothesis testing, correlation and regression, contingency tables

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.