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Unit of study_
COMP5329: Deep Learning
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.
| Code | COMP5329 |
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| Academic unit | Computer Science |
| Credit points | 6 |
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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 outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 1, 2024 [Normal evening] - Camperdown/Darlington, Sydney
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