ETW2500 - Unsupervised learning for business

Overview

Unsupervised learning for business is a specialised field within machine learning that focuses on extracting valuable insights and knowledge from unlabelled data in a business context. Unlike supervised learning, which relies on labelled data for training, unsupervised learning algorithms work with unstructured or unlabelled data to discover patterns, structures, or relationships that may not be immediately apparent. This unit explores various techniques and methodologies used in unsupervised learning to address specific business challenges and opportunities. It delves into applying these techniques to large and complex datasets, enabling businesses to make data-driven decisions and gain a competitive advantage.

Offerings

S1-01-MALAYSIA-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Prohibition

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Yalini Easvaralingam

Teaching approach

Active learning

Problem-based learning

Assessment

1 - Within semester assessment

Scheduled and non-scheduled teaching activities

Tutorials

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.