Overview

This unit aims to develop students’ conceptual and practical understanding of the field of artificial intelligence, machine learning and deep learning in the contexts of real-world applications. The students will learn about basic concepts, key techniques and popular tools and platforms, while many real-case scenarios will be presented to be practiced. They will gain the understanding of how to identify and define a machine learning task, what is required for resolving a machine learning project, and acquire the ability to apply given techniques and tools to resolve relevant practical tasks.

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

Learning outcomes

Students who successfully complete this unit will be able to:

  • Exhibit an in-depth understanding of core concepts, essential methods, and prevalent tools in machine learning
  • Illustrate comprehension of the stages involved in a practical machine learning project, and the ability to identify and define pertinent data science tasks in real-world situations
  • Conduct a thorough critical analysis, evaluation, and application of provided techniques and tools to address specified machine learning and deep learning challenges
  • Effectively communicate complex technical concepts to both technical and non-technical audiences in a professional manner

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On Campus
Lecture
2.00  4 weeks  8
Live Online
Lecture
2.00  8 weeks  16
On Campus
Class
2.00 12 weeks 24
Unspecified Activities
Independent Learning
8.50  12 weeks  102
TOTAL     150

Assessment

Type Task Weighting ULO's
Assignment Individual  40 - 50%  1,2,4 
Project Individual/Group 45 - 55%  1,2,3,4

Hurdle

The assessment of this Unit is composed of assignments, in-class tests, and the final project. 

To pass this Unit, students must achieve an overall grade of 50% or more, and meanwhile achieve at least 40% in the final assessment.

Students who do not achieve at least 40% in the final project will receive a maximum of 45% as the total mark for the unit.

Content

  • Foundations of artificial intelligence and machine learning in real world setting
  • Lifecycle of practical machine learning projects 
  • Data preparation for implementing machine learning techniques: concepts and tools 
  • Applications of given machine learning relevant techniques and tools to deal with specific machine learning relevant tasks 
  • Foundations of deep learning 
  • Basic concepts, techniques and tools of deep learning 
  • Applications of given deep learning relevant techniques and tools to deal with specific deep learning relevant tasks
  • Machine learning versus deep learning
  • Python-based implementation of ML/DL application 
 
  • Graduate Attribute – Communication Skills: Verbal communication
  • Graduate Attribute – Communication Skills: Communicating using different media
  • Graduate Attribute – Teamwork Skills: Teamwork roles and processes
  • Graduate Attribute – Digital Literacies: Information literacy
  • Graduate Attribute – Digital Literacies: Technical literacy

Study resources

Reading materials

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