Return to homepage Skip to navigation Skip to site search Skip to main content Skip to footer

Module Catalogue

SCQF Level: 09  

Module Code: CMP304

Credit Value: 20  

Year: 2017/8

Term: Term 2

School: School of Arts, Media and Games

Description

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.

Aims

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:

Pathfinding, including A* and its derivatives, Flocking and Steering, Rule Based Systems, Finite State Machines.

2 Academic AI Techniques:

Fuzzy Logic and Fuzzy State Machines, Genetic Algorithms, Artificial Neural Networks.

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 Header 6

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. 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 coursework and an examination. The coursework requires the student to develop an AI technique for a particular application. The student will then write a report on the activity and submit that with their application. In the report the student will critically evaluate the technique and their application of it.

Teaching and Learning Work Loads

Total 200
Lecture 28
Tutorial/Seminar 0
Supervised Practical Activity 14
Unsupervised Practical Activity 0
Assessment 80
Independent 78

Back


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.

Disclaimer: 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 2017/18 , and may be subject to change for future years.

Top