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This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.
Study level | Postgraduate |
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Academic unit | Computer Science |
Credit points | 6 |
Prerequisites:
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None |
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Corequisites:
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None |
Prohibitions:
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COMP5329 or COMP4329 |
Assumed knowledge:
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OCMP5318 or COMP5318 or COMP4318 |
At the completion of this unit, you should be able to:
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
Session | MoA ? | Location | Outline ? |
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Semester 1a 2024
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Online | Online Program |
View
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Semester 2a 2024
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Online | Online Program |
View
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Session | MoA ? | Location | Outline ? |
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PG Online Session 1A 2025
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Online | Online Program |
Outline unavailable
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PG Online Session 2A 2025
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Online | Online Program |
Outline unavailable
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Session | MoA ? | Location | Outline ? |
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Semester 2a 2023
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Online | Online Program |
View
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Find your current year census dates
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.