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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%