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Unit of study_
OCMP5329: 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:
Engineering
| Study level | Postgraduate |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
Prerequisites:
?
|
None |
|---|---|
|
Corequisites:
?
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None |
| Prohibitions:
?
|
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:
- LO1. Students will be able to demonstrate and build deep neural networks using fundamental layers and modules
- LO2. Students will be able to explain the principles and insights behind optimization and regularization techniques of deep neural networks
- LO3. Students will be able to compare different deep learning architectures, including MLP, CNN, RNN, Transformer, and GNN, as well as their characteristics
- LO4. Students will be able to implement and train various deep learning models with codes
- LO5. Student will get familiar with application scenarios and recent research of deep learning
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 1a 2024
|
Online | Online Program | |
|
Semester 2a 2024
|
Online | Online Program |
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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.
