LEO5314 · Machine learning and data analytics

Overview

Machine learning enables organisations to unlock insights and make informed predictions by leveraging predictive, diagnostic and prescriptive analysis. This unit covers the fundamentals of machine learning algorithms to analyse complex data with a specific focus on the use of R programming. The unit equips you with practical skills in supervised and unsupervised learning, including regression models, kNN, decision trees, random forests and clustering analysis. Examples of contemporary approaches to machine learning will be included through case across business disciplines.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. understand the principles of machine learning methods for diverse datasets
2. demonstrate ability in the use of machine learning algorithms for a range of business purposes
3. apply supervised learning for predictive analysis to support decision making
4. apply unsupervised learning for diagnostic analysis and prescriptive analysis to identify patterns and trends to derive actionable insights.

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.

Student experience, not official rules. Nothing here changes what the Handbook says.