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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 | Undergraduate |
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Academic unit | Computer Science |
Credit points | 6 |
Prerequisites:
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(DATA3888 or COMP3888 or COMP3988 or CSEC3888 or SOFT3888 or ENGG3112 or SCPU3001) and (COMP3308 or COMP3608 or COMP4318 or BMET2925) |
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Corequisites:
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Enrolment in a thesis unit INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103 |
Prohibitions:
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COMP5329 or OCMP5329 |
Assumed knowledge:
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A major in a computer science area |
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 1 2024
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Normal evening | Camperdown/Darlington, Sydney |
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Session | MoA ? | Location | Outline ? |
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Semester 1 2025
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Normal evening | Camperdown/Darlington, Sydney |
Outline unavailable
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