Machine Learning - entry

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. 

PRE-REQUISITE MODULES ECM1701

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 classification, regression and preliminary concepts;

Statistical preliminaries: Bayes’ theorem, uncertainty and information entropy, decision theory;

Density estimation and discriminants: KNN classifiers, parametric and semi-parametric methods;

Regression and classification: linear regression, common neural network architectures, MLPs, RBF network Kernel methods: basics of convex optimization, support vector machine, ridge regression and general regularization networks

Parameter estimation: maximum likelihood estimators, Bayesian learning, optimisation in practice;
Unsupervised methods: clustering, PCA, ICA;

Feature extraction: PCA, ICA, sequential forwards/backwards selection;

Computational algorithms for big data: gradient-descent and its accelerated proximal algorithms, online convex programming, stochastic gradient descent algorithm, implementation issues.

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: