ACS323 Intelligent Systems
This module will introduce students to the theme of intelligent systems with special applications to modelling, control, and pattern recognition. Although this technological area can be perceived as being broad, the focus will mainly be on Fuzzy Systems and on interesting synergies such as those between Fuzzy Systems and Artificial Neural Networks (ANN), including the Neuro-Fuzzy architecture. This module should appeal to all students from engineering as well as from science backgrounds who wish to learn more about Artificial Intelligence and Machine-Learning related paradigms, and mostly, how may the related architectures be applied effectively to solve real-world problems, i.e. non-linear, noisy, and the ones that are characterised by uncertainties. This unit is also timely indeed, since knowledge transfer from human to machine and from machine to human and knowledge extraction from data (Big Data) are seen particularly, as vital components for a successful economy, healthy well-being, and clean environment. Finally, the module strikes the too-often difficult balance between theoretical foundations and examples of applications via weekly interactive lectures, laboratory experiments, video demonstrations, and problem solving
Credits: 10 (Autumn semester)
If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.
Outside of lectures please contact me via email, or drop in to see me.
By the end of the module students will be able to:
This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.
Learning and Teaching Methods
Lectures and Tutorials: 24 hours
Learning and Teaching Materials
All teaching materials will be available via MOLE.
Assignment - 30%
No resit examination is available for this module.
Although this module does not include marked assignments, it does however involves interactive sessions between the Module Leader (MM) and all students as follows:
Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.