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Unit information: Uncertainty Modelling for Intelligent Systems in 2016/17

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Unit name Uncertainty Modelling for Intelligent Systems
Unit code EMATM1120
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

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 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

  1. To give an understanding of what approaches are available in uncertainty modelling, and under what conditions they can be appropriately applied.
  2. To give students familiarity with the advanced mathematics underlying different approaches to uncertainty modelling.
  3. To provide insight into the practical application of uncertainty modelling

Teaching details

Lectures

Assessment Details

2-hour written examination: 100% (all learning outcomes)

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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