TRC6901 Applied artificial intelligence
Faculty of Engineering
TRC6901 Applied artificial intelligence is a level 6, 0-credit-point, postgraduate unit from the Faculty of Engineering. It isn't offered in 2027. It has no prerequisites.
- Credit points
- 0
- Offered in 2027
- Not offered
- Assessment
- No exam
- 3 tasks
- Workload
- 144 hours
- per semester
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Requisites
Before TRC6901
Prohibitions
You can't enrol if you have passed any of these.
After TRC6901
No unit lists TRC6901 as a prerequisite in the 2027 handbook.
Enrolment rules
You must be enrolled in the Engineering PhD program. Students enrolled in another PhD program may seek permission from the Faculty of Engineering.
The unit assumes that you have basic programming skills. The unit assumes that you have the necessary foundational knowledge.
Overview
The unit provides an engaging exploration of the artificial intelligence (AI) domain, emphasising both traditional and generative AI. You will delve into the principles of traditional AI, acquiring skills in classification and regression tasks, essential for solving real-world problems in industrial settings. Subsequently, the unit transitions into the realm of generative AI, offering insights into its transformative applications within manufacturing and service industries, where autonomous data generation takes centre stage. The unit also examines rational and non-relational databases, elucidating their pivotal functions in storing, retrieving and optimising data for AI applications. Furthermore, the unit strongly emphasises ethical considerations in implementing generative AI, fostering a deeper understanding of the responsible use of AI technologies. Through a synergistic blend of theoretical foundations and hands-on applications, you will acquire the skills to develop intelligent solutions with AI technologies in the workplace.
Offerings in 2027
The 2027 handbook lists no offerings for TRC6901.
Assessment
- Computer labs40%
- Tests and quizzes30%
- Project30%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Apply appropriate denoising algorithms and feature engineering techniques to pre-process raw data.
- 2
Construct a comprehensive analytical pipeline employing machine or deep learning techniques for classification and regression tasks.
- 3
Design a natural language processing pipeline with artificial intelligence techniques to interpret human language.
- 4
Create content, such as images and slides, using generative artificial intelligence tools while adhering to ethical considerations relevant to industry applications.
- 5
Appraise relational and non-relational databases with generative artificial intelligence tools to cultivate innovative solutions.
- 6
Communicate artificial intelligence solutions tailored to real-world problems with a wider audience through oral presentations and written reports.
Workload and teaching
- Teaching approachProblem-based learning
- Teaching approachOnline learning
- Teaching approachActive learning
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
To better understand the concepts and principles, you will work by yourself or in a small group to solve real-world problems with artificial intelligence.
You are expected to prepare for the week by studying lecture notes and watching the pre-recorded videos.
This unit includes interactive workshops and computer laboratories for you to enhance your knowledge learned in lectures through individual or group participation in the activity.
Learning resources
Required resources
- Lecture slides and videos
- Workshop and laboratory materials
Common questions
What are the prerequisites for TRC6901?
TRC6901 has no prerequisites, but enrolment rules apply.
When is TRC6901 offered?
TRC6901 has no offerings listed in the 2027 handbook.
How much work is TRC6901?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does TRC6901 have an exam?
No. TRC6901 has 3 assessment tasks and no exam.