Aims and Fit of Module

This module aims to provide students with the necessary knowledge and practical skills in deploying a machine learning pipeline. This module is curated to cater for both students with or without science and engineering background. The module provides an overview on the different established machine learning frameworks and allows the students to implement them through the use of appropriate software tools. It shall generally cover different supervised and unsupervised models as well as the deployment of the models.

Learning outcomes

A. Critically assess and synthesise recent advances in robotics intelligence and machine learning, demonstrating a thorough understanding of emerging trends and technologies.
B. Systematically design, implement, and critically evaluate various advanced robotics and machine learning techniques in real-world applications, considering performance metrics such as accuracy and computational efficiency.
C. Develop machine learning-based robotics intelligence systems that meet specified objectives such as performance optimisation, cost-efficiency, and real-world applicability, societal impact, and articulate the rationale behind design choices.
D. Accurately define practical engineering problems, applying advanced machine learning methodologies to analyse, model, and solve these problems, and critically appraise the effectiveness of the chosen solutions.

Method of teaching and learning

The teaching philosophy of the module adopts the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contributions from industry partners. 

This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially the use of problem-based assessment which is project-focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. Group assessments aligns to the Code of Practice of Assessment and follow the regulation of assessing individualized performance to the required standard.

This module will be delivered through lectures, seminars, tutorials, and lab practice. Teaching will be conducted using problem-based learning, with problems, approaches, guided and unguided practices embedded in the sessions. General transferable skills are developed through the presentation of written and oral reports.