ETC3555 - Statistical machine learning - 2018

6 points, SCA Band 3, 0.125 EFTSL

Undergraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.


Business and Economics

Organisational Unit

Department of Econometrics and Business Statistics

Chief examiner(s)

Dr Souhaib Ben Taieb


Dr Souhaib Ben Taieb

Unit guides



  • Second semester 2018 (On-campus)


ETC3250 or FIT3154.


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.


The learning goals associated with this unit are 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.


Within semester assessment: 40% + Examination: 60%

Workload requirements

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.

See also Unit timetable information