1 x 120-minute lecture weekly
1 x 60-minute tutorial weekly
1 x 120-minute computer lab weekly
Enrolment not permitted
1 of COMP2712, COMP8715 has been successfully completed
Assumed knowledge
Computing and programming skills at a second year level, such as acquired in COMP2711 Computer Programming 2, or equivalent. Students without the assumed knowledge should check with the topic coordinator as to the background required, as there will be no additional assistance to compensate for missing background.
Topic description
Computational Intelligence (CI) is about solving complex problems without having to rely on explicit human knowledge. Nature has been a major inspiration for the constituent methodologies of CI, which include artificial neural networks, genetic and evolutionary computation, fuzzy systems, and swarm intelligence. The concepts, paradigms, algorithms, and implementations that define and distinguish each of these are the focus of this topic. While the material is introductory, insights into many new developments on the CI research front are provided and an emphasis is placed on real-world applications throughout. Specifically, the use of CI in machine vision and general pattern recognition tasks, including face recognition and other biometrics, is investigated. In addition, hybrid and ensemble strategies that combine multiple methodologies are also discussed. The topic teaches in a rigorous and practical way the set up and evaluation of CI experiments, building on the introduction to learning paradigms provided in Intelligent Systems. Students completing the topic will gain the necessary skills to develop and apply CI techniques towards solving complex problems in engineering, computer science, and beyond.
Educational aims
This topic provides a theoretical and practical introduction to the field of Computational Intelligence (CI). Students will understand the mathematical and algorithmic principles behind CI and gain hands-on experience in employing CI techniques towards solving practical problems in a variety of domains. The scope of the topic incorporates increasingly relevant applications such as biometrics and computer vision and the ethical issues associated with these. Students will also obtain skills in analytical thinking, experimental research, and effective communication within the wider context of CI.
Expected learning outcomes
At the completion of the topic, students are expected to be able to:

  1. Understand literature and applications associated with Computational Intelligence (CI) and communicate effectively in this field
  2. Appreciate the common principles and best practices linking the constituent domains of CI

  3. Utilize and integrate CI techniques towards solving a variety of relevant real-world problems
  4. Evaluate and compare in a scientific manner the suitability and effectiveness of different CI techniques
  5. Deal with the practical and ethical constraints associated with CI-based systems