Course Details in

Module Title	Introduction to Neural Computation
School	Computer Science
Department	Computer Science
Module Code	06 12412
Module Lead	Per Kristian Lehre
Level	Masters Level
Credits	10
Semester	Semester 1
Pre-requisites
Co-requisites
Restrictions	May not be taken in conjunction with Neural Computation; Pre-Requisite: A-Level Maths or equivalent
Contact Hours	Lecture-23 hours Total: 23 hours
Exclusions	None
Description	Through both lectures and practical work, the module introduces the basic concepts and techniques of neural computation and, more generally, automated learning in computing machines. It covers various forms of formal neurons and their relation to neurobiology, showing how to construct larger networks of formal neurons and study their learning and generalisation in the context of practical application. Finally, neural-based learning techniques are contrasted with other state of the art techniques of automated learning.
Learning Outcomes	On successful completion of this module, the student should be able to: Understand the relationship between real brains and simple artificial neural network models; Describe and explain the learning and generalisation aspects of neural computation; Apply neural computation algorithms to specific technical and scientific problems; Demonstrate an understanding of the benefits and limitations of neural based learning techniques in context of other state of the art methods of automated learning.
Assessment	12412-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%) 12412-02 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions	1.5 hour examination 80%, continuous assessment 20%. Supplementary: 1.5hr examination (80%) and continuous assessment.
Other	none
Reading List	Gurney K, An Introduction to Neural Networks 1997 Haykin S, 1999, Neural Networks: A Comprehensive Foundation (second edition), Prentice Hall Hastie T, Tibshirani R & Friedman J, 2001, the Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer CM Bishop, 1995, Neural Networks for Pattern Recognition, Oxford University Press