TRC5901 · Artificial intelligence applications in engineering

Overview

This unit offers an engaging exploration of artificial intelligence (AI) within engineering and industrial domains, focusing on both predictive and generative AI. You will study the principles of predictive AI through machine learning and deep learning techniques, developing engineering-specific skills in data preprocessing, classification and regression tasks. These skills are essential for solving complex industrial challenges by transforming raw data into meaningful insights. The unit then transitions to generative AI, providing an in-depth exploration of its transformative applications across various industries, where the creation of autonomous content enhances the efficiency and innovation of engineering processes and workflows. In addition, the unit places a strong emphasis on the ethical implementation of generative AI, fostering a nuanced understanding of responsible and sustainable AI practices. By integrating theoretical knowledge, data preprocessing techniques and practical engineering applications, you will gain the expertise required to design and implement intelligent solutions utilising 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 effective data preprocessing and feature engineering techniques to convert raw data into clean and usable formats.
2. Construct robust analytical pipelines utilising machine learning and deep learning methods to address classification and regression challenges specific to industrial needs.
3. Generate creative outputs, such as images and presentation slides, using generative AI tools while adhering to ethical standards and addressing industrial requirements.
4. Construct interactive chatbots utilising generative AI models and retrieval-augmented generation techniques to optimise processes and workflows in engineering domains.
5. Discuss AI-driven solutions tailored for industrial applications through engaging oral presentations and well-structured 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.