Introduction to Data and Machine Learning (MATH3856)

MATH3856 is a Mathematical Level III course.  See the course overview below. 

Units of Credit: 6

Prerequisites:  MATH2801 or MATH2901

Exclusion:  COMP9417 and ZZSC5836 

Cycle of offering:  Term 3

Additional Enrolment Constraints

A recommended prerequisite is MATH2831 Linear Models or MATH2931 Higher Linear Models.  Additionally, competency in at least a programming language such as R or Python is strongly recommended.

More information:  This recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus. Please refer to the web link at the top of the course offerings table. 

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 up-to-date timetabling information.

If you are currently enrolled in MATH3856, you can log into UNSW Moodle for this course.

This undergraduate course is expected to give students an understanding of the fundamentals of machine learning and the basics of data mining, which is essential for anyone contemplating a career as a professional statistician or data analyst in industries reliant upon such expertise.

The student should develop a working knowledge of the statistical and theoretical underpinnings of the topics covered. Given this fundamental statistical understanding of these methodologies, this will allow the student to utilise these techniques with confidence on real-world data sets and scenarios. As such the student is expected to develop an applied working knowledge of the methodologies covered, largely through practical applications. In addition, students will undertake additional reading of a collection of associated research papers on each topic, to further add context to the methodologies presented during the course. This will enhance the student’s ability to utilise these techniques to solve real-world problems. It is stressed that this course is aimed at fundamental statistical properties of these methods, it is not a course on the application of computer software.

A wide range of statistical methods and computational tools have been developed in the past few decades to gather information from data. This undergraduate course covers the key techniques in data mining and machine learning with theoretical background and applications, delivered through a series of lectures and tutorials. The topics include methods such as linear and logistic regression, neural networks, Bayesian neural networks, clustering and dimensionality reduction, ensemble learning, and an introduction to deep learning. Emerging machine learning tools and libraries are used to illustrate the methods in programming environments that include Python and R.