ETC5250 · Introduction to machine learning

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 about conceptualising problems using the bias-variance trade-off and how to balance this when fitting models. Complex model fitting techniques will be covered including bagging, boosting, cross-validation, regularisation and constructing ensembles. An important component is learning how to diagnose your model, especially utilising high-dimensional visualisation methods, and explain your model with explainable artificial intelligence (XAI). You will develop practical skills in applying techniques to different problems using a suitable software environment that involves doing reproducible analyses.

Offerings

Assessment

The Handbook lists an examination for this unit.

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

Requisites

Learning outcomes

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.

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.