Download Approved Module Information for Introduction to Computational

Approved Module Information for CS2320, 2014/5
Module Title/Name: Introduction to Computational
Intelligence
Module Code: CS2320
School: Engineering and Applied Science
Module Type: Standard Module
New Module? No
Module Credits: 10
Module Management Information
Module Leader Name
Email Address
Chris Buckingham
[email protected]
Telephone Number
Not Specified
Office
Not Specified
Level Description:
Level 5 (Foundation Degree/Dip He)
Available to Exchange Students?
Not Specified
Module Learning Information
Module Aims:
* Explore the goals and challenges of Computational Intelligence and its possible future implications.
* Introduce the main techniques and applications in Computational Intelligence.
* Provide a foundation for more specialised applications of Computational Intelligence.
Module Learning Outcomes:
* The history and major achievements of Computational Intelligence (CI), including its roots in Artificial Intelligence (AI).
* The distinctive properties of problems requiring CI applications and the techniques most appropriate for solving them.
* Programming languages and their properties that make them particularly appropriate for CI applications.
* The main application areas of CI and AI, their specific approaches, and seminal achievements.
* Ability to recognise problem structures most suited to computational intelligence approaches.
* Ability to apply the most appropriate problem representations and knowledge processing methods.
* Problem solving and reasoning using mathematical approaches (e.g. probabilities, logic, sets).
* How humans think and reason, including introspection and meta-analysis.
* Understanding of dynamic systems, emergent properties, and autonomous programming.
Links to Research:
The GRiST research programme, www.egrist.org
The Aston Lab for Intelligent Collectives Engineering (ALICE)
Module Delivery
Methods of Delivery & Learning Hours (by each method):
Method of Delivery
Lecture:
Lab Session:
Independent Study:
Total Learning Hours:
Learning Hours
22 hours
8 hours
70 hours
100 hours
Learning & Teaching Rationale:
Lectures are a mixture of content delivery and class exercises that equate to tutorials. These are designed to provide the
knowledge framework and conceptual understanding required for the practical session in the laboratory. In the labs, the students
will apply their knowledge to produce two types of computational intelligence applications.
Module Assessment
Methods of Assessment & associated weighting (including approaches to formative assessment as well as summative):
Assessment
Type
Category
Duration/
Submission
Date
Common
Modules/
Exempt from
Anonymous
Marking
Details
February to
June Exam
-
-
-
Details
Assessment
Weight
100%
Total:
Method of Submission:
Hard Copy Only
Assessment Rationale:
Written examination (summative) and lab classes (formative)
Feedback Rationale:
* Summative assessment by a single 2 hour examination.
* Formative assessment by class exercises in the lectures and laboratory sessions.
100%