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Unit of study_
COMP5329: Deep Learning
2024 unit information
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.
Unit details and rules
Managing faculty or University school:
Computer Science
| Code | COMP5329 |
|---|---|
| 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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COMP4329 OR OCMP5329 |
| Assumed knowledge:
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COMP4318 OR COMP5318 |
At the completion of this unit, you should be able to:
- LO1. demonstrate knowledge of the broad range of deep learning applications, such as image classification, object detection, image segmentation and face recognition
- LO2. use deep learning software to create deep learning prototypes
- LO3. evaluate deep learning algorithms
- LO4. demonstrate knowledge of the main methods of deep neural network design and evaluation and the relative strengths and weaknesses of each, and their most appropriate uses
- LO5. model application problems as deep learning problems
- LO6. apply and tailor known deep learning algorithms for solving new challenging problems
- LO7. present the design and evaluation of a deep learning prototype, defining the requirements, describing the design processes and evaluation.
Unit availability
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 ? |
|---|---|---|---|
|
Semester 1 2023
|
Normal evening | Camperdown/Darlington, Sydney | |
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Semester 1 2023
|
Normal evening | Remote |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2024
|
Normal evening | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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.
