Artificial Intelligence for Engineering
Duration
- One Semester or equivalent
Contact hours
- 48 hours face to face + Blended
On-campus unit delivery combines face-to-face and digital learning.
Prerequisites
100 cp in BEng or BCompSc or related double degrees
AND
Aims and objectives
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.
Unit Learning Outcomes (ULO)
On successful completion of this module the learner will be able to:
1 Design, build and train datasets using machine learning algorithms to solve multidisciplinary engineering problems (A4, A5, A6, K1, K2, S1).
2 Demonstrate knowledge of a range of AI, machine learning and deep learning algorithms and their applications (A1, A2, A4, K3, K4, S2)
3 Assess, appraise and justify appropriate AI techniques to solve computational engineering problems (A3, K4, K5, K6, S2, S3).
4 Communicate effectively and succinctly through oral presentations and reports (A2, A6, A7, K6, S3, S4).
Unit information in detail
- Teaching methods, assessment, general skills outcomes and content.
Teaching methods
| All Applicable Locations | |||||
|---|---|---|---|---|---|
| Activity Type | Activity | Total Hours | Number of Weeks | Hours Per Week | Optional - Activity Details |
| Face to Face Contact | Seminar | 12 | 12 weeks | 1 | |
| Face to Face Contact | Studio | 24 | 12 weeks | 2 | |
| Face to Face Contact | Other | 12 | 12 weeks | 1 | Facilitator meetings |
| Unspecified Learning Activities | Independent Learning | 102 | 12 weeks | 8.5 | |
| Total Hours: | 150 | Total Hours (per week): | 12.5 | ||
Assessment
| Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
| Portfolio | Individual | 40-60% | 1,2,3 |
| Design Project | Individual/Group | 40-60% | 1,2,3,4 |
Hurdle
To pass this unit, you must:
(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.
General skills outcomes
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 and references.
Reading materials
References
A list of reading materials and/or required texts will be made available in the Unit Outline.