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Pattern Recognition and Machine Learning (11482.1)

Level: Level 3 - Undergraduate Advanced Unit
Credit Points: 3
HECS Bands:

Band 2 2013-2020 (Expires 31 Dec 2020) Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021)

Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology

Availability

Possible changes to your unit's learning activities and assessment items

As a result of the Australian Government's and or the ACT Government’s directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, you may find that learning activities and/or assessment items in some units you are studying have changed. These changes will not be updated in the published Unit Outline but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. The new learning activities and/or assessment items will continue to meet the unit's learning outcomes, as described in the Unit Outline.

New learning activities and/or assessment items are available on your unit's UCLearn(Canvas) teaching site. Please contact your Unit Convener with any questions.

Unit Outlines

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  • Semester 2, 2020, ON-CAMPUS, BRUCE (192296) - View

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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.
This unit may be cotaught with 11512 Pattern Recognition and Machine Learning PG.

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.

Assessment Items

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

None.

Incompatible Units

11512 Pattern Recognition and Machine Learning PG.

Equivalent Units

None.



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