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

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