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Unit of study_

COMP5329: Deep Learning

2025 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:
? 
None
Prohibitions:
? 
COMP4329 or OCMP5329
Assumed knowledge:
? 
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 2024
Normal evening Camperdown/Darlington, Sydney
Session MoA ?  Location Outline ? 
Semester 1 2025
Normal evening Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 1 2020
Normal evening Camperdown/Darlington, Sydney
Semester 1 2021
Normal evening Remote
Semester 1 2022
Normal evening Camperdown/Darlington, Sydney
Semester 1 2022
Normal evening Remote
Semester 1 2023
Normal evening Camperdown/Darlington, Sydney
Semester 1 2023
Normal evening Remote

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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.