Applied Machine Learning

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

Hawthorn

26-February-2024
26-May-2024

10-March-2024

31-March-2024

12-April-2024

02-July-2024

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)
Face to Face Contact (Phasing out) Lecture	2.00	13 week	26
Face to Face Contact (Phasing out) Computer Laboratory	2.00	13 week	26
Unspecified Learning Activities (Phasing out) Individual Study	7.54	13 week	98
TOTAL			150

Assessment

Type	Task	Weighting	ULO's
Portfolio	Individual	100%	1,2,3,4,5
Portfolio	Individual	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.