MATH5836 is an honours and postgraduate mathematics course.

Units of credit: 6

Exclusions: COMP9417, ZZSC5836

Cycle of offering: Term 3 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: This recent Course Outline (PDF) contains information about course objectives, assessments, course materials and the syllabus. 

The online handbook entry contains information about the course. The timetable is only up-to-date if the course is being offered this year.

If you are currently enrolled in MATH5836, you can log into UNSW Moodle for this course.

Course overview

Increasingly, organisations need to analyse enormous data sets to determine useful structures in them. In response to this, a range of statistical and machine learning methods have been developed in recent times. This course covers the key techniques in data mining and machine learning with theoretical background and applications. The topics include methods such as linear and logistic regression, neural networks, Bayesian neural networks, clustering and dimensionality reduction, ensemble learning, and also provides an introduction to deep learning. Emerging machine learning tools and libraries are used to illustrate the methods in programming environments that includes Python and R.

The course is recommended by the professional association of data miners, the Institute of Analytics Professionals of Australia.