Overview
This unit develops your ability to model multi-dimensional data using statistical and machine learning techniques. Topics covered include: dimension reduction with linear and nonlinear methods; supervised learning such as discriminant analysis, decision trees and forests, neural networks; and unsupervised learning such as k-means, hierarchical and model-based clustering. You will learn … For more content click the Read More button below.
Offerings
S1-01-CLAYTON-BLENDED
Requisites
Prerequisite
Prohibition
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Jack Jewson
Learning outcomes
On successful completion of this unit, you should be able to:
1.
develop, select, and diagnose statistical and machine learning methods for supervised and unsupervised tasks
2.
measure the uncertainty of a prediction or classification using resampling methods
3.
efficiently conduct analysis tasks in a contemporary software environment
4.
explain and interpret the analyses undertaken clearly and effectively
5.
apply analytic tools to contemporary business problems.
Teaching approach
Active learning
Problem-based learning
Assessment
1 - Within semester assessment
2 - Examination
Scheduled and non-scheduled teaching activities
Seminars
Tutorials
Workshops
Workload requirements
Workload
Learning resources
Technology resources
Other unit costs
Costs are indicative and subject to change.
Electronics, calculators and tools: $100