Module Information

Module Identifier
Module Title
Academic Year
Semester 1
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Presentation and discussion of analytic report on scientific paper(s)  20%
Semester Assessment Essay - topic in Intelligent Systems (3000 words).  80%
Supplementary Assessment Will take a form as agreed by Departmental exam board  100%

Learning Outcomes

On successful completion of this module students should be able to:

Describe and use the basic principles of Artificial Intelligence and Machine Learning

Be able to reflect on project needs

Practically apply AI and ML principles to meet those needs

Present the material they have learned in an informed, clear manner

Demonstrate understanding and insight into the material that they are presenting

Brief description

This module introduces the key ideas in Artificial Intelligence and ensures all students are at roughly the same level before moving on to the specialist modules.


1. Introduction (3 hrs)
General introduction to Artificial Intelligence (AI), including discussion of what AI is, its history, definitions, and philosophical debates on the issue (the Turing test and the Chinese room). Ethical issues.
2. Search (6 hrs)
Why search is important in AI and how to go about it. This includes both informed and uninformed strategies. Evolutionary search.
3. Knowledge Representation (4 hrs)
Ways of representing knowledge in a computer-understandable way. Semantic networks, rules. Examples of the importance of KR.
4. Neural networks and subsymbolic learning (5hrs)
We can find solutions using search, but how can we remember solutions, learn from them and adapt them to new situations? This will cover perceptrons, single-layer and multi-layer networks.
5. Propositional and First-Order Logic (4 hrs)
The backbone of knowledge representation.
6. Programming for Intelligent Systems (3 hrs)
Practical introduction to programming for Intelligent Systems, used to illustrate search, KR and first-order logic.
7. Rule-based systems (3 hrs)
How can human expertise be automated? How to build an expert system - system concepts and architectures. Rule-based systems: design, operation, reasoning, backward and forward chaining.
8. Knowledge Acquisition and it importance in KR and RBS. (2 hrs)


This module is at CQFW Level 7