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Unit information: Uncertainty Modelling for Intelligent Systems 3 in 2020/21

Unit name Uncertainty Modelling for Intelligent Systems 3
Unit code EMAT30015
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Lawry
Open unit status Not open
Pre-requisites

EMAT10704 Discrete Mathematics 1, or equivalent

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

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 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

  1. IL01 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.
  2. ILO2 Students will be able to use the mathematics underlying different approaches to uncertainty modelling to explain the assumption underpinning them and to identify the relationships between them
  3. ILO3 Students will be able to describe how different approaches to uncertainty modelling could be applied to reasoning and knowledge representation problems.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Details

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

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