DEPARTMENT OF ENGINEERING MATHEMATICS
EMATM1120
UNCERTAINTY MODELLING FOR INTELLIGENT SYSTEMS
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DEPARTMENT OF ENGINEERING MATHEMATICSEMATM1120UNCERTAINTY MODELLING FOR INTELLIGENT SYSTEMS |
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| Timetable | Assessment | Syllabus | Materials | Textbooks | Past exams |
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Organiser: |
Dr Jonathan Lawry |
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Lecturer: |
Dr Jonathan Lawry |
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Description: |
This
unit will explore the techniques and methodologies developed within Artificial
Intelligence to represent and reason with information which is uncertain,
imprecise or fuzzy. The unit will provide an overview of a range of different
approaches explaining both the mathematics and the underlying philosophy and
investigating practical applications. |
| Pre-requisites: | None |
| Co-requisite: | None |
| Aims: |
•To
provide students with an overview of uncertainty modelling techniques and
formalisms •To
provide students with an in-depth study of the mathematics and philosophy
underlying these techniques •To
provide a detailed analysis of the application of uncertainty modelling in
intelligent systems. |
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Learning outcomes: |
•To give
an understanding of what approaches are available and under what conditions
they can be appropriately applied. •To give students familiarity with the advanced mathematics underlying different approaches. •To provide insight into the practical application of uncertainty modelling |
| Check timetabling | |
| End
of year exam (90%) and short proejct (10%). |
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| Materials: | Lecture materials are on Blackboard |
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Probabilistic
Reasoning in Intelligent Systems, The uncertain reasoner's companion, - a mathematical
perspective, Jeff Paris, Cambridge Tracts in Theoretical Computer Science. Modelling and Reasoning with Vague Concepts, J. Lawry, Springer A
first course in fuzzy logic, Hung T. Nguyen and Elbert A. Walker |
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| Past exams: | See http://www.enm.bris.ac.uk/teaching/local/exam-p-and-s/exams-index.html |