Module title: Computational Intelligence

SCQF level: 10:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET10107
Module leader: Kevin Sim
School School of Computing
Subject area group: Software Engineering
Prerequisites

SET09117
Algorithms and Data Structures

Examples of Equivalent Learning: Algorithms and Data Structures based knowledge within a high level programming language.

2019/0, Trimester 2, Face-to-Face, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: Face-to-Face
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Kevin Sim
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
Learning & Teaching methods including their alignment to LOs
A core lecture series will introduce concepts and theory, and be augmented by specialist case-study lectures given by researchers in the field where possible. Lectures will include video material and demonstrations where appropriate (LO1, LO2). Practical sessions will utilise a mixture of lab-based sessions to prepare for the coursework, in which students will have the opportunity to develop their own implementations of algorithms as well as customising and analysing existing software (LO3, LO4).

Assessment (formative or summative)
Formative assessment will take place during weekly exercise to be given out in practical sessions which will allow students to assess their current understanding of material and identify gaps in their knowledge. Summative assessment takes place via an examination (LO1,2) at the end of the module and a coursework (LO3,4); the coursework will require the students to demonstrate that they understand both the practical and theoretical aspects of the field, and as such will involve both writing and demonstrating code as well as writing an academic report. Coursework will involve the practical implementation of a computational intelligence technique, and writing a report in which the results are evaluated appropriately (LO3, 4). Practical classes will provide support for this.



Formative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Summative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Centrally Time Tabled Examination 2
Face To Face Lecture 24
Face To Face Practical classes and workshops 24
Independent Learning Guided independent study 150
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 60 3,4 12 HOURS= 0, WORDS= 48
Centrally Time Tabled Examination 40 1,2 14/15 HOURS= 2, WORDS= 0
Component 1 subtotal: 60
Component 2 subtotal: 40
Module subtotal: 100

Description of module content:

In this module you will:
- develop an awareness of the use of modern computational intelligence techniques;
- develop an understanding of a number of techniques such as evolutionary algorithms, neural networks, co-evolution, classifier systems and fuzzy logic;
- gain practical experience of implementing and evaluating computational intelligence algorithms.

Learning Outcomes for module:

Upon completion of this module you will be able to
LO1: Critically evaluate the use of soft computational intelligence techniques.
LO2: Compare and contrast the use of differing techniques to achieve particular functionalities.
LO3: Specify, implement and customise a computational intelligence algorithm.
LO4: Apply appropriate evaluation techniques in order to analyse an algorithm’s effectiveness.

Indicative References and Reading List - URL:

Core - EIBEN, A AND SMITH J (2003) INTRODUCTION TO EVOLUTIONARY COMPUTING: SPRINGER, 1st ed.
Core - NEGNEVITSKY, M (2002) ARTIFICIAL INTELLIGENCE: A GUIDE TO INTELLIGENT SYSTEMS: ADDISON-WESLEY, 2002nd ed.
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