Deep Learning Applications

Postgraduate | 2026

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Area/Catalogue
COMP 6004
Course ID icon
Course ID
202515
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
2
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

This course will provide students with an advanced understanding of deep learning applications by exploring the theory and practical implementation of deep neural networks.

Course learning outcomes

  • Explain the fundamental concepts and theories of deep learning, including the architecture and functioning of neural networks
  • Design, implement, and train convolutional neural networks (CNNs) for image processing and classification tasks
  • Develop recurrent neural networks (RNNs) and their variants (LSTM, GRU) for sequential data processing and natural language processing applications
  • Apply advanced deep learning techniques such as transfer learning, generative adversarial networks (GANs), and reinforcement learning to enhance model performance
  • Evaluate the performance of deep learning models using appropriate metrics and techniques, and optimise models for improved accuracy and efficiency
  • Integrate and apply deep learning knowledge and skills in a comprehensive capstone project, demonstrating the ability to solve complex real-world problems

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A