MATH5836 is a Mathematics Level V course. See the course overview below.

Units of credit: 6

Prerequisites:

Cycle of offering: each year in Session 1

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

More information: this recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus. (This pdf will usually be updated by the end of the first week of the session.)

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH5836, you can log into the My eLearning Vista instance of this course.

Increasingly, organisations need to analyse enormous data sets to determine useful structure in them. Data sets of interest arise in a vast number of applications such as medical diagnosis, genetics, digital image correction, image recognition, marketing, loan financing, insurance and fraud detection as well as research in the social sciences. In response to this, a wide range of statistical methods and tools have been developed in recent times to allow accurate and fast analysis of these sets.

Topics include: choosing the right data mining tool for your data, clustering methods, decision trees, multivariate adaptive regression splines, hybrid models, neural networks, support vector machines, bagging and boosting methods.

Case studies of industry-based data mining projects will feature prominently. The most recent data mining commercial software including CART, MARS and SAS Enterprise Miner will be used to illustrate most methods.

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