Key facts
UNE unit code: COSC380
*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
- Yes
- No
- Yes
- 6
Unit information
Machine learning is at the forefront of development in computer science, presenting us with unprecedented opportunities to harness the power of artificial intelligence. From medical research to security, to language translation and self-driving cars, its applications in daily life are rapidly increasing.
This unit introduces you to some of the main algorithms used in machine learning.
Using key results from basic probability as a foundation, topics include inferring variables from data, clustering data, mixture models for clustering data, Monte Carlo methods for modelling probability distributions, Bayesian neural networks for classification, and Hopfield neural networks for modelling associative memories.
Emphasising strategies to derive algorithms with nice properties that can be proved, you will learn how to implement these algorithms for simple data sets.
Assignments addressing theory and programming components are designed to help you to fine tune skills that are in demand.
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 | Online |
*Offering is subject to availability
Intensive schools
There are no intensive schools required for this unit.
Enrolment rules
Notes
Please note that it is necessary to have a basic background in probability, calculus, and linear algebra before taking this unit.
Please note that this unit does not cover software libraries such as tensor flow. All algorithms covered will be fully implemented by the student.
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:
- demonstrate programming skills in a high-level programming language;
- demonstrate knowledge in algorithms related to machine learning;
- critically assess and appraise the approaches presented in this unit; and
- demonstrate problem-solving and algorithm-development skills.
Assessment information
Assessments are subject to change up to 8 weeks prior to the start of the teaching period in which you are undertaking the unit.
| Title | Must Complete | Weight | Offerings | Assessment Notes |
|---|---|---|---|---|
| Assessment | Yes | 10% | All offerings | Math/theory/programming tasks. All assessment tasks must be attempted. |
| Assessment 1 | Yes | 5% | All offerings | Math/theory/programming tasks. All assessment tasks must be attempted. |
| Assessment 3 | Yes | 25% | All offerings | Math/theory/programming tasks. All assessment tasks must be attempted. |
| Final Examination - Assurance Task | Yes | 60% | All offerings | It is mandatory to pass this component in order to pass the unit. There is a supervised exam at the end of the teaching period in which you are enrolled. The exam will be offered online with supervision via webcam and screen sharing technology. Coordinated by UNE Exams Unit. |
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.
Pattern Recognition and Machine Learning
ISBN: 9780387310732
Bishop, C., Springer 2006
Text refers to: All offerings
Information Theory, Inference and Learning Algorithms
ISBN: 9780521642989
MacKay, D.J.C., Cambridge University Press 2003
Text refers to: All offerings
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