Aims and Fit of Module

This module aims to gives an introduction on neural networks, their architectures and their applications.
Students will be equipped with a broad expertise in the basic principles, techniques, algorithms, implementation and applications of neural networks. They will learn to implement, train and debug their own neural networks to solve real-world problems.

Learning outcomes

A Develop an understanding of neural networks – their architectures, applications and limitations.
B Demonstrate the ability to implement neural networks with a programming language
C Demonstrate the ability to provide critical analysis on real-world problems and design suitable solutions based on neural networks.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. The module will be delivered in a combination of lectures, seminars and tutorials. Lectures will introduce students to the academic content. Seminars/tutorials will be used to expand the students understanding of lecture materials. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading.