ETC3555 - Statistical machine learning

There is a more recent version of this academic item available.

Overview

This unit covers the methods and practice of statistical machine learning for modern data analysis problems. Topics covered will include recommender systems, social networks, text mining, matrix decomposition and completion, and sparse multivariate methods. All computing will be conducted using the R programming language.

Offerings

S2-01-CLAYTON-ON-CAMPUS

Requisites

Prerequisite

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Klaus Ackermann

Learning outcomes

On successful completion of this unit, you should be able to:

identify and understand the statistical and computational trade-offs in modern data analysis problems

develop computer skills for exploring modern data sets

understand and apply machine learning algorithms to solve modern data analysis problems.

Assessment

1 - Within semester assessment

2 - Examination

Scheduled and non-scheduled teaching activities

Lectures

Tutorials

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. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

Other unit costs

Costs are indicative and subject to change.
Electronics, calculators and tools: $40