MATH5836 is an honours and postgraduate mathematics/statistics 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: The course outline will be linked within the course offering table for the term applicable. Outlines will be made available prior to the start of the applicable term. 

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

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.