Artificial Intelligence for Engineering
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
AND
100 credit points in BEng or BCompSc or related double degrees.
27-October-2024
01-June-2025
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 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Design Project | Individual/Group | 40 - 60% | 1,2,3,4 |
| Portfolio | Individual | 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 learningÂ
- Machine Learning techniques
- Designing an Algorithm for data preparation
- Specifications of Machine learning
- AI for future engineering technologies
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Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.