Neural Networks and Deep Learning

Undergraduate | 2026

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area/catalogue icon
Area/Catalogue
ARTI 3003
Course ID icon
Course ID
202961
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
3
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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.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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Note:
Course data is interim and subject to change

Course overview

The course aims to provide students with a comprehensive understanding of fundamental concepts in neural networks and deep learning with practical skills in model training, development, and validation. Building on prior knowledge of machine learning and its mathematical foundation, students will learn about various types of neural network architectures and deep learning. They will gain hands-on experience in programming deep learning systems using Python. By the end of the course, students will be proficient in designing, training, and evaluating deep learning models for various tasks, including classification, regression, and generative modelling on real-world datasets. This course provides students with the necessary skills and knowledge required by modern machine learning professionals and a solid foundation for further life-long learning in machine learning. 

Course learning outcomes

  • Select a suitable neural network architecture for a given problem
  • Implement neural network architectures in a modern framework
  • Apply the machine learning pipeline to neural network models on complex datasets
  • Evaluate different deep learning architectures for specific tasks
  • Apply deep learning models for natural language processing and computer vision
  • Present results and recommendations to stakeholders

Prerequisite(s)

Corequisite(s)

N/A

Antirequisite(s)

N/A