ETC3555 Statistical machine learning
Faculty of Business and Economics
ETC3555 Statistical machine learning is a level 3, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2023 in Semester 2 at Clayton. It needs FIT3154, ETX3250 or ETC3250.
- Credit points
- 6
- Offered in 2023
- Semester 2
- Clayton
- Assessment
- Exam 60%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2023 handbook entry. See the 2027 entry.
Reviews
No reviews yetNo reviews yet. Be the first to review ETC3555.
Requisites
Before ETC3555
Prerequisites
Pass these before you enrol.
After ETC3555
No unit lists ETC3555 as a prerequisite in the 2023 handbook.
Enrolment rules
To be successful in this unit, background knowledge and application of maths is required at the equivalent of VCE Year 12 Higher level. You may have satisfied this by completing relevant prerequisite unit/s, or you have covered relevant topics in your final years of secondary study. You should self-assess your maths competency prior to enrolling in this unit.
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. 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 in 2023
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
Assessment
- Within semester assessment40%
- ExaminationThreshold hurdle60%
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
- 3
understand and apply machine learning algorithms to solve modern data analysis problems.
Workload and teaching
- Tutorials18 hours
- Seminars24 hours
- Teaching approachActive learning
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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
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.
Where it fits
ETC3555 is part of 2 areas of study in the 2023 handbook.
Contacts
- Chief Examiners
- Dr Klaus Ackermann
Common questions
What are the prerequisites for ETC3555?
You need FIT3154, ETX3250 or ETC3250 before you enrol. Enrolment rules also apply.
When is ETC3555 offered?
In 2023, ETC3555 runs in Semester 2 at Clayton.
How much work is ETC3555?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETC3555 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 1 other task.
Which majors and minors include ETC3555?
ETC3555 is part of Business analytics.