Introductory description

This module offers a practical, project-based exploration of how Artificial Intelligence (AI) can be applied to solve challenges in modern industrial settings. The core of the module involves training a machine learning model and deploying it on accessible edge computing hardware. The module will aim to cover the entire workflow from data collection with sensors to intelligent, real-time decision-making.
When this module is delivered on any overseas programme, it is delivered in a 2-week block rather than over 4 weeks.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Sensing and Data Acquisition
2. Applied Machine Learning
3. Edge Computing (TinyML)
4. Integration and Evaluation

Learning outcomes

By the end of the module, students should be able to:

• Appraise appropriate sensors and communication methods to capture a representative dataset for demonstrating a solution to a defined manufacturing problem.
• Design data analysis techniques to develop and validate a machine learning model that addresses the core requirements of a chosen proof-of-concept.
• Evaluate the principles of edge computing and demonstrate the deployment of a trained AI model onto a target hardware platform to perform basic inference.
• Integrate the core components—sensing, data processing, and an embedded AI model—into a cohesive proof-of-concept that demonstrates a simplified, end-to-end intelligent workflow.
• Evaluate the functionality of the developed proof-of-concept, analysing its strengths, limitations, and the necessary steps required to scale it into a viable, real-world industrial system.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

Data Extraction; Data Analysis; ML Model Building and Deployment

Transferable skills

Team Work; Technical Communication; Problem Solving