Overview

The purpose of this unit is to introduce students to the industrial applications of artificial intelligence, with a focus on machine learning. Students will gain an understanding of the business, societal and industrial benefits of AI. They will learn techniques to model and manage data using contemporary technologies and apply statistical techniques to evaluate the output.

Requisites

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

  • Demonstrate an understanding of problem-solving using programming skills.
  • Demonstrate an understanding of artificial intelligence concepts and the practical applications
  • Apply basic principles, models and algorithms in artificial intelligence to solve problems in Industry 4.0 contexts
  • Identify real-world problems and select the most appropriate artificial intelligence method to solve the problem
  • Examine ethical issues which arise when artificial intelligence is applied.

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Laboratory
4.00 11 weeks 44
Face to Face Contact (Phasing out)
Laboratory
4.00 11 weeks 44
Placement
Placement
8.00 1 week 8
Online
Directed Online Learning and Independent Learning
4.00 1 week 4
Unspecified Learning Activities (Phasing out)
Individual Study
50.00 1 week 50
TOTAL150

Assessment

Type Task Weighting ULO's
AssignmentIndividual 20 - 30% 1,2,3 
PresentationGroup 10 - 20% 1,2,3,4,5 
ProjectGroup 10 - 20% 1,2,3,4,5 
Test 1Individual 10 - 20% 3,4 

Content

Introduction to  Programming Principles (e.g. Python,XML, SQL, R)
Introduction to  Machine Learning  and AI
Supervised  & Unsupervised Learning
 Regression analysis
 Probability and Statistics
AI/ML Development Tools Analytics
Applications of AI in Advanced Manufacturing
Benefits and Risks of AI implementation

Study resources

Reading materials

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