Module Information

Module Identifier
Module Title
Academic Year
Semester 1
Available to final year MEng students only
Other Staff

Course Delivery

Delivery Type Delivery length / details
Seminars / Tutorials 30 hours
Practical 10 hours


Assessment Type Assessment length / details Proportion
Semester Assessment Written assessment of scientific papers  Oral presentation and discussion of analytic report on scienctific paper(s)  (up to 3000 words), followed by oral presentation and discussion.  40%
Semester Assessment Report (up to 3000 words)  topic in Intelligent Systems - 3000 words  on contrasting the application of (at least) two ML methods to an identified dataset.  60%
Supplementary Assessment Resubmission of failed/nonsubmitted coursework components or ones of equivalent value.  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.


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

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 hours
General intorduction 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 hours
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 hours
Ways of representing knowledge in a computer-understandable way. Semantic networks, rules. Examples of the importance of KR.
4. Neural networks and subsymbolic learning - 5 hours
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 hours
The backbone of knowledge representation.
6. Programming for Intelligent Systems - 3 hours
Practical introduction to programming for Intelligent Systems, used to illustrate search, KR and first-order logic.
7. Rule-based Systems - 3 hours
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 its importance in KR and RBS - 2 hours

Module Skills

Skills Type Skills details
Application of Number Inherent to subject
Communication Seminar
Improving own Learning and Performance Inherent to subject
Information Technology Inherent to subject
Personal Development and Career planning Encourages students to see roles in subject for career and personal development
Problem solving Inherent to subject
Research skills Essay
Subject Specific Skills Advanced Artificial Intelligence skills
Team work


This module is at CQFW Level 7