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for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Uncertainty Modelling for Intelligent Systems (CDT) |
| Unit code |
EMATM0060 |
| Credit points |
10 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12)
|
| Unit director |
Professor. Lawry |
| Open unit status |
Not open |
| Pre-requisites |
None
|
| Co-requisites |
None
|
| School/department |
School of Engineering Mathematics and Technology |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
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.
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.
Intended Learning Outcomes
- Students will be able to explain a range of different approaches that are available in uncertainty modelling, and identify under what conditions they can be appropriately applied.
- Students will be able to use the advanced mathematics underlying different approaches to uncertainty modelling to explain the assumption underpinning them and to identify the relationships between them.
- Students will be able to apply different approaches to uncertainty modelling to reasoning and knowledge representation problems.
Teaching Information
Teaching will be delivered through a series of mostly synchronous sessions, including lectures, seminars, practical activities, discussion groups and self-directed exercises.
Assessment Information
1 Summative Assessment, 100% - Coursework. This will assess all ILOs.
Reading and References
- Probabilistic Reasoning in Intelligent Systems, Judea Pearl, Morgan Kaufmann.
- 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
