Computer Science

Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition.  Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming. 

In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.

In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops.  Neural Networks, Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques) where it is important to have explicit rules governing machine learning and pattern recognition. Recent development of techniques and algorithms for big-data analysis will also be addressed.

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

Discipline Specific Skills and Knowledge

Personal and Key Transferable / Employment Skills and Knowledge

Introductory material: Practical motivation for machine learning, basic ideas of supervised and unsupervised learning, classification, regression

Describing data

Latent descriptions: k-means, maximum likelihood; mixture models; PCA; ICA

Unsupervised learning: Clustering; Locality Sensitive Hashing

Supervised models: k-nearest neighbours, linear and non-linear regression, linear discriminant analysis, logistic regression,

SVM and maximum margin classifiers

Loss functions and maximum likelihood estimators

Bayesian learning & sampling

Neural nets and deep learning

Evaluation of performance, dataset balance

Ensemble methods: boosting, bagging, decision trees and random forests

Metric learning

Markov decision processes: Reinforcement learning (Q-learning)

Referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 50% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Reading list for this module: