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.
A Demonstrate the understanding of different types of machine learning architectures B Formulate and analyse the performance of different supervised learning models C Formulate and analyse the performance of different unsupervised learning models D Formulate and analyse rudimentary deep learning architecture E Demonstrate the ability to deploy machine learning models
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 contribution 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. 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.