ETC5555 Statistical machine learning
Faculty of Business and Economics
ETC5555 Statistical machine learning is a level 5, 6-credit-point, postgraduate unit from the Faculty of Business and Economics, offered in 2026 in Semester 2 at Clayton. It needs ETC3250, ETF5932, ETC5250, FIT3154 or ETX3250.
- Credit points
- 6
- Offered in 2026
- Semester 2
- Clayton
- Assessment
- Exam 45%
- and 2 other tasks
- Workload
- 144 hours
- per semester
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before ETC5555
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After ETC5555
No unit lists ETC5555 as a prerequisite in the 2026 handbook.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit covers the methods and practice of statistical machine learning for modern data analysis problems. You will take a deep look at the procedure of learning from data with particular focus placed on how to effectively learn model parameters and methods to guard against overfitting. Topics covered will include stochastic gradient descent, deep neural networks with dropout, convolutional neural networks for image recognition, and text mining and generation with recurrent neural networks. All computing will be conducted using open source software. Introductory machine learning methods such as linear models, decision trees, random forests, and hierarchical clustering, are assumed.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | Blended |
Assessment
- Exercise15%
- Project40%
- Examination45%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
identify and understand the statistical and computational trade-offs in modern data analysis problems
- 2
develop computer skills for exploring modern data sets and applying state-of-the-art machine learning algorithms
- 3
understand and apply machine learning algorithms to solve modern data analysis problems.
Workload and teaching
- Tutorials12 hours
- Workshops12 hours
- Seminars24 hours
- Teaching approachActive learning
- Teaching approachResearch activities
- Teaching approachSimulation or virtual practice
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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit allows you to develop your research skills by engaging in structured inquiry using a systematic approach and discipline-specific methodologies.
This unit allows you to engage in guided, immersive experiences to develop relevant skills, knowledge and attitudes through simulation or virtual practice.
Learning resources
Technology resources
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.
Contacts
- Chief Examiners
- Jack Jewson
Common questions
What are the prerequisites for ETC5555?
You need ETC3250, ETF5932, ETC5250, FIT3154 or ETX3250 before you enrol.
When is ETC5555 offered?
In 2026, ETC5555 runs in Semester 2 at Clayton.
How much work is ETC5555?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETC5555 have an exam?
Yes. The exam is worth 45% of the final mark, alongside 2 other tasks.