Handling Uncertainty with Fuzzy Sets and Fuzzy Systems
| Code | School | Level | Credits | Semesters |
| COMP4033 | Computer Science | 4 | 20 | Autumn UK |
- Code
- COMP4033
- School
- Computer Science
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
This module focusses on handling uncertainty such as vagueness using fuzzy sets and similar approaches. It provides a thorough understanding of key topics such as the nature of uncertainty captured by fuzzy sets and associated links to human reasoning, inference using fuzzy sets, similarity of fuzzy sets, design and modelling of information via fuzzy sets, type-1 fuzzy sets, type-2 fuzzy sets, fuzzy logic systems, and fuzzy set-based applications.
Students will also be exposed to some of the cutting-edge research topics in uncertain data and decision making, e.g. capturing uncertainty in real-world scenarios, modelling it, and aggregating (uncertain) information from multiple sources. Students will develop practical systems and software in a suitable programming language.
Target Students
Available to Level 3 and Level 4 students in the School of Computer Science. This module is not available to students not listed above without explicit approval from the module convenor(s).Students will be doing some programming, so some computer programming experience is recommended. Support in the course will cover the subject material and not programming skills. Please discuss with the module convenor if you are not sure about these requirements.This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Classes
Lecture to be timetabled before the Computing session in the week.
Assessment
- 35% Coursework 1: Software implementation of a fuzzy system, including a written report, 2000 words. The reassessment for this module will be 100% Examination.
- 65% Exam 1 (2-hour): Answering questions on the topics taught in the lectures.
Assessed by end of autumn semester
Educational Aims
To understand how and when uncertainty and vagueness in data can be usefully captured by fuzzy systems.To understand fuzzy sets and fuzzy systems in practice.To be able to design and implement a fuzzy system.To understand relevant modelling and aggregation techniques for capturing and handling uncertainty in real-world data.Learning Outcomes
Knowledge and Understanding
- Ability to describe fuzzy sets and systems.
Intellectual Skills
- Understand fuzzy logic and where it might be applied.
Professional Skills
- To be able to know when fuzzy systems might be deployed, and to be able to implement such a system.
Transferable Skills
- Problem solving and written communication.