Artificial Intelligence


This module provides an introduction to some of the many Artificial Intelligence techniques which are currently, or could in the near future, be used to enhance the development of intelligent systems applied to various application domains.


The aim of this module is to provide the student with knowledge of how artificial intelligence techniques can be used effectively within an application environment to provide intelligence and/or the illusion of intelligence.

Learning Outcomes

By the end of this module the student should be able to:

1.  Critically examine various artificial intelligence techniques.

2.  Develop a critical understanding of AI techniques and technologies.

3.  Evaluate the use of AI technologies and techniques for specific purposes.

Indicative Content

1 ‘Traditional’ AI:

Rule Based Systems, Finite State Machines.

2 Academic AI Techniques:

Fuzzy Logic and Fuzzy State Machines, Case Basde Reasoning, Genetic Algorithms, Reinforcement Learning, Probabilistic Techniques, Artificial Neural Networks, Clustering Algorithms.

3 Applications of AI:

Combining AI techniques to produce A-life and Intelligent Agents.

4 Machine Learning:

The ability of a machine to learn from its environment

5 Mining

Knowledge discovery and the process of finding hidden patterns in data

6 Big Data

The challenge of the 21st century is ‘too much data and not enough analysis’. Explore the challenges and opportunities afforded by this phenomenon.

7 Intelligence on the Internet:

Analyse the emergence of intelligent agents on the internet.

Statement on Teaching, Learning and Assessment

Contact time is split equally between lectures, tutorials and practical sessions. The tutorials will take the form of worked examples of the techniques and discussions of their practical applications. The practical sessions will comprise directed self-study of the techniques and will give the students the opportunity to look at the techniques in more depth and experiment with their application. The learning outcomes will be assessed by a portfolio of coursework and an examination. The coursework requires the student to develop AI techniques for particular applications. The student will then write a report on each activity and submit that with their applications. In the reports the student will critically evaluate the technique and their application of it.

Teaching and Learning Work Loads

Total 200
Lecture 24
Tutorial/Seminar 24
Supervised Practical Activity 24
Unsupervised Practical Activity 0
Assessment 80
Independent 48

Guidance notes

Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.


We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2019/10 , and may be subject to change for future years.