Key facts
UNE unit code: AMTH405
*You are viewing the 2027 version of this unit which may be subject to change in future.
- Trimester 2 - On Campus
- Trimester 2 - Online
- Armidale Campus
- UNE Sydney Campus
- Yes
- No
- Yes
- 6
Unit information
Artificial intelligence tools allow us to identify patterns in data, make predictions of complex phenomena in the face of uncertainty, generate text or multimedia content, and solve problems that are difficult for humans. These tools rely on computational algorithms built upon core mathematical ideas, and so understanding and applying AI tools requires familiarity with those mathematical foundations.
This fundamental unit will introduce you to the most commonly used mathematical ideas in Artificial Intelligence and Machine Learning, giving you the necessary background to progress towards using and understanding advanced techniques. Topics covered include: calculus, which enables optimisation and the quantification of uncertainty; linear algebra, which is the foundation of how we structure data and perform high-dimensional calculations; graphs and trees, which enable abstraction and search that are fundamental to programming; and linear regression, the foundational model that many modern tools generalise.
Offerings
For further information about UNE's teaching periods, please go to Principal Dates.
| Teaching period | Mode/location |
|---|---|
| Trimester 2 | On Campus, Armidale Campus |
| Trimester 2 | On Campus, UNE Sydney Campus |
| Trimester 2 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Please refer to the student handbook for current details on this unit.
Unit coordinator(s)
Learning outcomes
Upon completion of this unit, students will be able to:
- calculate derivatives and integrals of fundamental functions such as polynomials, exponential, and logarithmic functions, in one and many variables, using standard techniques;
- determine local and global extrema of functions of one variable using standard calculus methods;
- conduct mathematical operations on matrices and vectors, and use these tools to solve systems of linear equations;
- conduct operations on graphs and trees;
- apply, evaluate and interpret low-dimensional linear regression; and
- demonstrate a high level of understanding, and broad theoretical and technical knowledge, by synthesising ideas from calculus, linear algebra, discrete mathematics and statistics to apply to case studies in data science and artificial intelligence.
Learning resources
Textbooks are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
Note: Recommended material may be held in the University Library — purchase is optional.
Mathematics for Machine Learning
ISBN: 9781108455145
Deisenroth, M. P., Faisal, A. A., & Ong, C. S., Cambridge University Press 2020
Text refers to: Trimester 2 (On Campus, Armidale Campus), Trimester 2 (On Campus, UNE Sydney Campus), Trimester 2 (Online)
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