Cybernetics and Neural Networks (100H6)

15 credits, Level 7 (Masters)

Autumn teaching

This is an introductory course that lays the foundations for further self-study.

Emphasis is placed on analysis of basic neural network architectures and learning rules. The course spends significant time exploring training of neural networks. The utilisation of artificial intelligence techniques in neural networks is also explored.

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 included. Engineering design skills, programming skills in a high level language.

Many of the illustrations have been simplified to demonstrate principles to facilitate understanding.

The syllabus covers the following AHEP4 learning outcomes:

M1, M2, M3, M4, M5, M6, M12

Teaching

77%: Lecture
23%: Practical (Laboratory)

Assessment

20%: Coursework (Report)
80%: Examination (Computer-based examination)

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 regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2022/23. However, there may be changes to these modules in response to COVID-19, staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses: