Overview

This unit equips students with the essential skills to implement applied machine learning projects. It focuses on techniques and methods used in real world applications, rather than on machine learning theories and statistics.

Requisites

Prerequisites
COS30018 Intelligent Systems

OR
COS30019 Introduction to Artificial Intelligence

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

Learning outcomes

Students who successfully complete this unit will be able to:

  • Explain machine learning life cycle
  • Use appropriate data engineering techniques for data preparation
  • Analyse and apply advanced machine learning algorithms to solve real-world problems
  • Evaluate, deploy and optimise machine learning solutions for given problems
  • Interpret and effectively communicate machine learning project outcomes to domain-specific users

Teaching methods

Sarawak

Type Hours per week Number of weeks Total (number of hours)
Live Online
Lecture		 1.00  12 weeks  12
Online
Lecture		 1.00 12 weeks 12
On-Campus
Class		 2.00  12 weeks  24
Unspecified Activities
Independent Learning		 8.5  12 weeks  102
TOTAL     150

Assessment

Type Task Weighting ULO's
PortfolioIndividual 100% 1,2,3,4,5 
PortfolioIndividual 100% 1,2,3,4,5 

Content

  • Machine learning life cycle
  • Advanced supervised learning
  • Semi-supervised and transfer learning
  • Deep learning and reinforcement learning
  • Data engineering and pre-processing techniques
  • Model optimisation
  • Model evaluation, deployment and maintenance
  • Machine learning project documentation and communication

Study resources

Reading materials

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