### 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.

### Requisites

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
26-February-2024
26-May-2024
Last self-enrolment date
10-March-2024
Census date
31-March-2024
Last withdraw without fail date
12-April-2024
Results released date
02-July-2024
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

### 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

ExaminationIndividual 50 - 60% 1,2,4
Laboratory ReportIndividual 5 - 10% 1,2,3,4
Mid-Semester TestIndividual 10 - 20%
Mid-Semester TestIndividual 10 - 20% 2,3
Online QuizzesIndividual 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.