Computer Science

COM2014 - Computational Intelligence (2019)

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MODULE TITLEComputational Intelligence CREDIT VALUE15
MODULE CODECOM2014 MODULE CONVENERUnknown
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;

• Logic and knowledge representation: first-order logic and logic programming; reasoning strategies; use of 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; Dempster-Shafer theory; fuzzy logic operators and process;

• Searching for solutions: hill-climbing, local search and simplex search; decision trees (searching databases); minimax algorithm (searching game spaces); A* (searching maps etc.);

• 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; verification and validation.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 35.00 Guided Independent Study 115.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 20 Lectures
Scheduled Learning and Teaching 15 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 30 Written Exams 70 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 70 2 Hours 1-3, 5-6, 8 Orally, on request
Technical Exercise and Report 10 10 hours 2, 4, 6-8  Written
Technical Exercise and Report 20 20 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
All Written Exam All August Ref/Def Period

 

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