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Pattern Recognition and Machine Learning (11482.1)

Level: Level 3 - Undergraduate Advanced Unit
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology

Availability

Syllabus

Pattern recognition, machine learning and deep learning are closely related topics in the field of artificial intelligence (AI) and its applications in robotics, computer vision, natural language processing, data science, and many others. This unit is an advanced unit in artificial intelligence that focuses on the core element of modelling and recognising patterns in data through learning. Application areas are in analysing data from images and video, healthcare, finance, sports, text documents, speech, human-machine interaction and many more. This unit covers selected topics from Bayesian Inference, Deep Neural Networks, Support Vector Machines/Regression, Graphical Models, and Mixture Models as well as ethical and privacy considerations around the use of AI. Students will gain both an understanding of the theoretical foundations as well as hands-on experience in implementing and using machine learning techniques in real-world applications.

Learning Outcomes

After successful completion of this unit, students will be able to:

1. Understand, describe and critique pattern recognition, machine learning and deep learning techniques;

2. Identify and select suitable modelling, learning and prediction techniques to solve a problem;

3. Design and implement a machine learning solution; and

4. Appraise ethical and privacy issues of artificial intelligence techniques.

Contact Hours

Four hours of problem-based learning activities/interactive workshops/practical work in laboratory classes on-campus per week.

Prerequisites

Must have passed 48 credit points.

Corequisites

None.

Assumed Knowledge

Working knowledge of programming (e.g. scripting languages), discrete mathematics, algebra and numerical analysis.

Incompatible Units

11513 Pattern Recognition and Machine Learning PG

Equivalent Units

None.



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