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Pattern Recognition and Machine Learning PG (11512.1)

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

Availability

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

For the remainder of 2020, resulting from Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, any exams that are required for assessment in a unit will be online exams. Online exams may also use online proctoring to help assure the academic integrity of those exams. Please contact your unit convener with any questions.

While the University has made efforts to ensure that Unit Outlines reflect a unit’s learning activities and assessment items, any changes to Australian Government directives because of the COVID-19 pandemic may require changes to these during the semester to ensure the safety and well being of students and staff. 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. Any changes made will continue to meet the unit’s learning outcomes, as described in the Unit Outline.

Unit Outlines

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  • Semester 2, 2020, ON-CAMPUS, BRUCE (192332) - 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 will be co-taught with 11482 Pattern Recognition and Machine Learning.

Learning Outcomes

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

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

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

3. Design and implement a refined 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 and practical work in laboratory classes on campus per week.

Prerequisites

None.

Corequisites

None.

Assumed Knowledge

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

Incompatible Units

11482 Pattern Recognition and Machine Learning

Equivalent Units

None.


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