CS4047: COMPUTATIONAL INTELLIGENCE (2020-2021)

Last modified: 21 Oct 2020 08:45

Course Overview

Computational Intelligence covers a wide range of issues that developed in parallel with, or in competition to, symbolic AI. The major constituents of the field are bio-inspired computing â€“ which deals with an ever expanding number of biologically related techniques â€“ and fuzzy logic â€“ which deals with reasoning under conditions of vagueness. In this course we will explore a number of topics that are core to Computational Intelligence (e.g. neural nets and evolutionary computing) and these will lead into some state-of-the-art approaches (such as fuzzy model-based reasoning and learning).

Course Details

Study Type	Undergraduate	Level	4
Session	First Sub Session	Credit Points	15 credits (7.5 ECTS credits)
Campus	Aberdeen	Sustained Study	No
Co-ordinators	Professor George M. Coghill

Qualification Prerequisites

One of Programme Level 3 or Programme Level 4 or Programme Level 5

What courses & programmes must have been taken before this course?

One of CS3015 Software Engineering: Principles and Practice (Passed) or CS3024 Software Engineering and Professional Issues (Passed) or CS3528 Software Engineering and Professional Practice (Passed)
Any Undergraduate Programme (Studied)
One of CS3015 Software Engineering: Principles and Practice (Passed) or CS3024 Software Engineering and Professional Issues (Passed) or CS3028 Principles of Software Engineering (Passed)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

Course Description

A selection of topics spanning a range of Computational Intelligence approaches under the following headings:

Fuzzy Systems (e.g. Fuzzy Logic, Fuzzy Rule Bases, Mamdani Methods).
Model-based Technology (e.g. Qualitative and Fuzzy Qualitative reasoning, model-based diagnosis).
Nature Inspired Computing (eg. Neural Nets Artificial Immune Systems, Particle Swarm optimisation methods. This will include a rudimentary presentation of the basic biological principles involved).
Introduction to Machine Learning (e.g. Decision Trees, concept learning, clustering

In light of Covid-19 this information is indicative and may be subject to change.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

1 Tutorial during University weeks 9 - 18

More Information about Week Numbers

In light of Covid-19 and the move to blended learning delivery the assessment information advertised for second half-session courses may be subject to change. All updates for second-half session courses will be actioned in advance of the second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Critical Essay 50%

Research Project 50%

Alternative Resit Arrangements for students taking course in Academic Year 2020/21

Where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge Level	Thinking Skill	Outcome