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Unit of study_
COMP4329: 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 | COMP4329 |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
Prerequisites:
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COMP3308 or COMP3608 or COMP4318 or BMET2925 |
|---|---|
|
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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None |
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
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Modes of attendance (MoA)
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