Overview

This unit of study aims to develop analytical skills using mathematical methods of vector calculus, probability and statistics. The unit introduces students to fundamental mathematical ideas and techniques and illustrates their application for real-world settings.

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
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:

  • Apply vector calculus to analyse and model processes that arise in electrical engineering applications (K2).
  • Manipulate standard probability distributions and use the maximum likelihood method to estimate distribution parameters (K2)
  • Examine how statistical hypothesis testing can be applied to datasets and perform computation using MATLAB (K2)
  • Apply principal component analysis to extract the major features of noisy data (K2).

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Lecture		2.00 12 weeks 24
Live Online
Class		1.00 12 weeks 12
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
Examination Individual  50 - 60%  1,2,4 
Laboratory Report Individual  5 - 15%  1,2,3,4 
Mid-Semester Test Individual  10 - 20%  1 
Mid-Semester Test Individual  10 - 20%  2,3 
Online Quizzes Individual  5 - 15%  1,2,3,4 

Content

  • Vector Calculus
  • Principal component analysis
  • Probability (random variable, distributions, central limit theorem)
  • Maximum likelihood method
  • Statistical hypothesis testing
  • Manipulation of datasets and statistical analysis in MATLAB.

Study resources

Reading materials

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