Study information

Computational Intelligence - 2024 entry

MODULE TITLEComputational Intelligence CREDIT VALUE15
MODULE CODECOM2014 MODULE CONVENER Guoqiang Zhang (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Computational intelligence is the science of computational systems that are able to perform specific tasks, adapting to particular data. The module will equip you to design and use computational intelligence to solve a variety of problems such as planning, scheduling, optimisation, using a variety of techniques including biologically inspired computational, fuzzy logic, agent-based models and simulation.

Pre-requisite Modules: COM2013 (Data Science Group Project 2); ECM1400; MTH1004

AIMS - intentions of the module

The aim of this module is to introduce and give you practice in some of the main areas of computational intelligence that can be used to solve problems arising in data science. It aims to give you and understanding of the theoretical basis of these methods and their relation to other artificial intelligence techniques. Specifically, it will introduce classical “crisp” logic and knowledge representation before proceeding to fuzzy logic to cope with uncertain and vague processes. Searching and optimisation arise in many contexts and this module aims to introduce you deterministic and stochastic optimisation methods, particularly evolutionary optimisation.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Explain the nature of Computational Intelligence, its scope, and its limitations;

2 Display competence in a range of Computational Intelligence tools and techniques;

3 Explain the theoretical basis for a range of Computational Intelligence methods;

4 Make use of Computational Intelligence methods in practical applications;

Discipline Specific Skills and Knowledge:

5 Describe a number of different programming paradigms;

6 Learn a variety of data science methods and apply them to real problems;

Personal and Key Transferable / Employment Skills and Knowledge:

7 Plan and write a technical report;

8 Adapt existing technical knowledge to learning new methods.

SYLLABUS PLAN - summary of the structure and academic content of the module
 
• Introduction: history and context of computational intelligence, artificial intelligence and related disciplines;
 
• Searching for solutions: Depth-first Search, Breath-first Search, Greedy Best-first Search, A* Search, MiniMax Search, MiniMax Search with Alpha-Beta Pruning
 
• Logic and knowledge representation: Propositional Logic, first-order logic and reasoning strategies; use of propositional and first-order logic as a knowledge representation language;
 
• Fuzzy logic: measurements and modelling in the face of incomplete knowledge; vagueness and uncertainty. Fuzzy set theory;  fuzzy logic operators and process;
 
• Evolutionary computation: population-based stochastic search; genetic algorithms, representations and operators; exploration and exploitation;
 
• Agent-based models and simulation: cellular automata; state-based models; complex systems and emergent behaviour
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 35 Guided Independent Study 115 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 22 Lectures
Scheduled Learning and Teaching 13 Workshops and tutorials
Guided Independent Study 115 Coursework; private study; reading

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Not Applicable      

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written Exam 60 2 hours (Summer) 1-3, 5-6, 8 Orally, on request
Coursework 1 40 30 hours 2, 4, 6-8  Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Written Exam Written Exam (2 hours) 1-3, 5, 6, 8 August Ref/Def Period
Coursework 1 Coursework 1 2, 4, 6, 7, 8 August Ref/Def Period

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework and/or written exam in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Ross, T. Fuzzy Logic with Engineering Applications 4th Wiley 2016 978-1119235866
Set Russell S. and Norvig P. Artificial Intelligence: A Modern Approach 3rd Edition Pearson 2010
Set Simon, D. Evolutionary Optimization Algorithms Wiley-Blackwell 2013 978-0470937419
Set Wilensky, U. and Rand, W. An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo MIT Press 2015
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1400, MTH1004, COM2013, COM1011, COM2011
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12th April 2019 LAST REVISION DATE Thursday 19th October 2023
KEY WORDS SEARCH None Defined

Please note that all modules are subject to change, please get in touch if you have any questions about this module.