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2018/19 Undergraduate Module Catalogue

COMP2611 Artificial Intelligence

10 creditsClass Size: 275

Module manager: Dr Ali Gooya

Taught: Semester 2 View Timetable

Year running 2018/19

Pre-requisite qualifications

COMP1421 Fundamental Mathematical Concepts
ELEC1704 Further Engineering Mathematics

This module is not approved as a discovery module

Module summary

Artificial intelligence is a developed field within computer science and is rapidly evolving. The foundations of this field have roots in the work of Alan Turing investigating the boundary between human intelligence and computers. Technologies developed in the field of artificial intelligence have found their way into everyday life and form services and infrastructure that we rely on on a day to day basis. Such services and infrastructure include internet search, predictive text, speech recognition and automation.This module covers the foundations of the topics in artificial intelligence and considers its uses in a wide range of applications as well as the ethical and legal issues that arise.


This module provides the foundations of artificial intelligence and considers the legal, ethical and social issues surrounding the use of artificial intelligence.

Learning outcomes
On successful completion of this module a student will have demonstrated the ability to:

- deconstruct the ethical arguments surrounding artificial intelligence and its applications.
- employ artificial intelligence techniques to solve real world problems.
- evaluate the effectiveness of artificial intelligence techniques when applied to real world problems.
- identify weaknesses and limitations of artificial intelligence techniques.


This module covers the following 3 topic areas:

- Artificial intelligence techniques : search techniques, logic, knowledge representation, probability, Markov models, Bayesian networks and genetic algorithms.
- Ethical issues : soft artificial intelligence, hard artificial intelligence, general artificial intelligence and singularity.
- Applications of artificial intelligence : text mining, game play and searching.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours60.00
Total Contact hours40.00
Total hours (100hr per 10 credits)100.00

Opportunities for Formative Feedback

Coursework and labs.

Methods of assessment

Assessment typeNotes% of formal assessment
PracticalLab Implementation10.00
ReportProject Report10.00
Total percentage (Assessment Coursework)20.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 00 mins80.00
Total percentage (Assessment Exams)80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 05/02/2019


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