TRC6901 · Applied artificial intelligence

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

The Handbook publishes no offerings for this unit.

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Continuous assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

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

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.

Student experience, not official rules. Nothing here changes what the Handbook says.