Cybernetics and Neural Networks (100H6)
15 credits, Level 7 (Masters)
A cybernetic device responds and adapts to a changing environment in a sensible way. Neural network systems permit the construction of such devices exploiting information, feedback and control to achieve intelligent interaction and behaviour from autonomous devices such as robots.
In this module the utilisation of artificial intelligence techniques and neural networks are explored in detail. Software implementation of theoretical concepts will solve genuine engineering problems in dynamic feedback control systems, pattern recognition and scheduling problems. In many instances solutions must be computed in response to data arriving in real-time (e.g. video data). The implications of high speed decision making will be explored.
The module will explore:
- neuron models
- network architectures
- perceptron and perceptron learning rule
- synaptic vector spaces
- linear transformations for neural networks
- supervised hebbian learning
- performance optimisation
- widrow-hoff learning
- associative learning
- competitive networks.
Learning will be supported by laboratories using the Matlab Neural Network Toolbox.
Teaching and assessment
We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 26 hours of contact time and about 124 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.
We’re planning to run this module in the academic year 2020/21. However, there may be changes to this module in response to COVID-19, or due to staff availability, student demand or updates to our curriculum. It may not be possible to take some module combinations due to timetabling constraints. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.
This module is offered on the following courses: