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Artificial Intelligence and Machine Learning


  • Credit value: 15 credits at Level 6
  • Module convenor and tutor: Professor George Magoulas
  • Prerequisites: knowledge of calculus and linear algebra
  • Assessment: online quizzes (30% and 70%)

Module description

Using a combination of lectures and lab work, this module introduces you to artificial intelligence and machine learning paradigms, giving you knowledge of fundamental aspects at the theoretical and practical levels.

The module covers computational algorithms for learning from data, data-driven decision making and complex problem solving. It provides an introduction to machine learning methods, such as neural networks, fuzzy logic, fuzzy clustering, bio-inspired computing, and covers basic concepts of feature selection and generalisation.


  • Knowledge-based systems
  • Fuzzy systems
  • Artificial Neural Networks
  • Supervised and unsupervised learning
  • Evolutionary computation

Learning objectives

By the end of this module, you will be able to:

  • discuss essential facts, concepts, principles and theories of artificial intelligence and machine learning methods
  • recognise social, ethical and professional issues and risk involved in the design and deployment of AI and machine learning methods in applications
  • describe and analyse the process of designing intelligent systems equipped with AI and machine learning components
  • apply theoretical knowledge of AI and machine learning paradigms to solve classification and decision-making problems
  • evaluate quality attributes and trade-offs when using AI and machine learning methods in a given problem.