Applied Artificial Intelligence and Machine Learning

Postgraduate | 2026

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Area/Catalogue
ARTI 5002
Course ID icon
Course ID
201716
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
1
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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 introductory course aims to equip students with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) through the practical application of algorithms using Python. Students will explore essential pre-deep learning techniques, focusing on implementing and interpreting AI and ML models, and understanding evaluation metrics. The course covers topics such as data preprocessing, supervised learning algorithms like regression and classification, unsupervised learning techniques such as clustering, and a basic introduction to neural networks. Designed to prepare students for advanced studies in deep learning, this course will enable them to apply, assess, and refine ML algorithms effectively, setting a robust foundation for tackling more complex AI challenges in future endeavours.

  • Foundations Of Ai And Supervised Learning
  • Model Evaluation And Advanced Learning Techniques
  • Unsupervised Learning And Neural Networks

Course learning outcomes

  • Comprehensive understanding of AI and ML principles, including basic Python applications
  • Proficiency in data preprocessing techniques (handling missing data, normalization, feature engineering)
  • Ability to critically assess ML models using evaluation metrics and techniques to address overfitting/underfitting
  • Capability to develop, apply, and tune supervised learning algorithms to real-world data
  • Mastery of unsupervised learning methods and their applications to discover patterns in data
  • Basic understanding of neural network architectures and their applications

Prerequisite(s)

Corequisite(s)

N/A

Antirequisite(s)

N/A