Courses :: Module, MACHINE LEARNING & NEURAL NETWORKS

	Overview
	Machine learning principles. Target functions and Inductive bias. Decision trees, ID3 and C4.5. Lazy learning, CBR and K Nearest neighbours, Biological nerons and Hebbian learning. Perceptrons and decision spaces. Multi layer-Perceptrons, and back-propagation learning. Recurrent neural networks, Hopfield neural networks. Stochastic networks. Evolutionary and Genetic computation.

Overview

Machine learning principles. Target functions and Inductive bias. Decision trees, ID3 and C4.5. Lazy learning, CBR and K Nearest neighbours, Biological nerons and Hebbian learning. Perceptrons and decision spaces. Multi layer-Perceptrons, and back-propagation learning. Recurrent neural networks, Hopfield neural networks. Stochastic networks. Evolutionary and Genetic computation.

	Learning Outcomes
	On successful completion of the module, students should be able to: Distinguish the different categories of Machine Learning techniques and identify situations in which they might be used; Describe the implementation of simple algorithms for machine learning; Discuss the adaptation of a standard technique to a given specific problem; Describe the implementation and evaluation some standard machine learning algorithms in a general purpose programming language such as Java; Obtain a basic understanding of machine learning approaches and neural networks; Discuss the relationships between training set, test set, generalisation, cross validation; Describe how various machine learning techniques work and what their strengths and limitations are; Select and use appropriate machine learning techniques to solve real problems.

Learning Outcomes

	Autumn Supplementals/Resits
	University scheduled written examination (Autumn): 120 minutes

Autumn Supplementals/Resits

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Courses / Module

MACHINE LEARNING & NEURAL NETWORKS

Semester 1

Semester 2