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  6. CS4047: COMPUTATIONAL INTELLIGENCE

CS4047: COMPUTATIONAL INTELLIGENCE (2017-2018)

Last modified: 25 May 2018 11:16


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 – which deals with an ever expanding number of biologically related techniques – and fuzzy logic – 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 None. Sustained Study No
Co-ordinators
  • Professor George 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 CS3028 Principles of Software Engineering (Passed)
  • Any Undergraduate Programme (Studied)
  • 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)

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?

No

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

Further Information & Notes

(i) Assistive technologies may be required for any student who is unable to use a standard keyboard/mouse/computer monitor. Any students wishing to discuss this further should contact the School Disability Co-ordinator.

Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

1st Attempt: 1 two-hour written examination (75%); continuous assessment (25%). Resit: One 2-hour examination (100%).

Formative Assessment

During lectures, the Personal Response System and/or other ways of student interaction will be used for formative assessment. Additionally, practical sessions will provide students with practice opportunities and formative assessment.

Feedback

Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.

Course Learning Outcomes

None.
 

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