Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks.

Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised machine learning.

(LO1) Learning  the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.

(LO2) Understanding of the neural network learning processes and their most popular types, as well as  appreciation of how neural networks can be applied to artificial intelligence problems.

(S1) On successful completion of this module the student should be able to pursue further study in artificial intelligence and more advanced types of neural networks.

(S2) On successful completion of this module the student should be able to analyse numerically the mathematical properties of most major network types and apply them to artificial intelligence problems.

(S3) On successful completion of this module the student should be able to approach methodically artificial intelligence problems and understand the principal mathematics of learning systems.

12 lectures delivering the following chapters:
Introduction
Chapter 1 Structural Aspects
Chapter 2 Learning Processes
Chapter 3 Single-Layer Perceptron
Chapter 4 Multi-Layer Perceptron
Chapter 5 Radial-basis Function Networks
Chapter 6 Support Vector Machines
Chapter 7 Self-Organising Maps
Chapter 8

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Tutorial
Description:
Attendance Recorded: Not yet decided