Overview

The unit of study aims to provide engineers with the knowledge and skills required to design and implement artificial intelligence, machine learning techniques that can effectively solve complex engineering problems. It is important for engineering professionals to understand the Artificial intelligence concepts and techniques for building intelligent systems.

Requisites

Prerequisites
COS10009 Introduction to Programming

AND
100 credit points in BEng or BCompSc or related double degrees.


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
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:

  • Design, build and train datasets using machine learning algorithms to solve multidisciplinary engineering problems (A4, A5, A6, K1, K2, S1).
  • Demonstrate knowledge of a range of AI, machine learning and deep learning algorithms and their applications (A1, A2, A4, K3, K4, S2).
  • Assess, appraise and justify appropriate AI techniques to solve computational engineering problems (A3, K4, K5, K6, S2, S3).
  • Communicate effectively and succinctly through oral presentations and reports (A2, A6, A7, K6, S3, S4).

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Seminar
1.00 12 weeks 12
Face to Face Contact (Phasing out)
Studio
2.00 12 weeks 24
Face to Face Contact (Phasing out)
Other
1.00 12 weeks 12
Unspecified Learning Activities (Phasing out)
Independent Learning
8.50 12 weeks 102
TOTAL150

Assessment

Type Task Weighting ULO's
Design ProjectIndividual/Group 40 - 60% 1,2,3,4 
PortfolioIndividual 40 - 60% 1,2,3 

Hurdle

As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:

(i) achieve an overall mark for the unit of 50% or more, and(ii) complete the project to an acceptable standard. A rubric will be used to determine if students have met the acceptable standard. The rubric is available on Canvas; and(iii) Achieve a minimum of 50% or more on the Portfolio (must pass at least 50% of the portfolio activities). Students who do not successfully achieve hurdle requirements (ii) and (iii) in full, will receive a maximum of 45% as the total mark for the unit.

Content

  • Different methods of Machine learning 
  • Machine Learning techniques
  • Designing an Algorithm for data preparation
  • Specifications of Machine learning
  • AI for future engineering technologies

 

Study resources

Reading materials

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