Introduction to Artificial Intelligence - COMP5280

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Autumn Term 5 15 (7.5) Palaniappan Ramaswamy checkmark-circle


This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. You learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.


Contact hours

Total contact hours: 28
Private study hours:122
Total study hours: 150


Autumn or Spring

Method of assessment

13.1 Main assessment methods
A1 – Practical assignment (25%)
A2 – Practical assignment (25%)
2 hour unseen written examination (50%)

13.2 Reassessment methods
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Indicative reading

S.J. Russell & P. Norvig, "Artificial Intelligence: A modern approach", 2nd Edition. Prentice-Hall, 2002. (main textbook)
S. Pinker. "How the Mind Works", W.W. Norton & Company, 1999.
A. Cawsey, “The Essence of Artificial Intelligence”, Prentice-Hall, 1998.
P. Bentley. “Digital Biology”, Simon & Schuster, 2002
R.L. Haupt & S.E. Haupt, “Practical Genetic Algorithms”, 2nd edition, Wiley, 2004.
S. Haykin, “Neural Networks and Learning Machines”, 3rd Edition. Pearson, 2009.

See the library reading list for this module (Canterbury)

Learning outcomes

On successfully completing the module students will be able to:
1. Explain the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature of intelligence and learning.
2. Describe and apply the main kinds of state-space search algorithms, considering their strengths and limitations.
3. Explain the main concepts and principles associated with different kinds of knowledge representation, such as logic, case-based representations, and subsymbolic/connectionist representations.
4. Explain the differences between the major kinds of machine learning problems – namely supervised learning, unsupervised learning and reinforcement learning – and describe and implement the basic ideas of algorithms for solving those problems.
5. Describe the main concepts and principles of major kinds of biologically-inspired algorithms, and understand and implement one such technique.
6. Describe how various intelligent-system techniques have been used in the context of several case studies, and compare different techniques in the context of those case studies.


  1. Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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