ETC3250 - 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

S1-01-CLAYTON-BLENDED

Requisites

Prerequisite

Prohibition

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Jack Jewson

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

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.

Learning resources

Technology resources

This unit will use R and RStudio. Please download and install these two software systems on your own computer. Instructions will be given in the first lecture. Install R first (it is like the airplane) and then Rstudio (it is like the airport terminal). Details on installation can be found on the course web site.

There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.

Other unit costs

Costs are indicative and subject to change.
Electronics, calculators and tools: $100