Computer Science

Artificially intelligent  machines and software must assimilate data from their environment and make decisions based upon it.  Likewise, we live in a data-rich society and must be able to make sense of complex datasets.  This module will introduce you to machine learning methods for learning from data.  You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. The emphasis throughout is on dealing with real data and you will use, modify and write software to implement learning algorithms.  It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.

Prerequisite modules: ECM1401 or ECM1408, ECM1701, ECM1707

The goal of artificially intelligent machines requires that machines and software must assimilate data from their environment and make decisions based upon it. This module aims to equip the student with the fundamentals of machine learning in a computer science context. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods.  Particular attention is paid to methods for visualising complex datasets.

Module Specific Skills and Knowledge:
1 apply principles for statistical pattern recognition to novel data;
2 analyse novel pattern recognition and classification problems, establish models for them and write software to solve them;
3 apply a range of supervised and unsupervised pattern recognition and machine learning techniques to a wide range of problems;
Discipline Specific Skills and Knowledge:
4 state the importance and difficulty of establishing principled models for pattern recognition;
5 use Matlab or other programming languages for scientific analysis and simulation;
Personal and Key Transferable/ Employment Skills and  Knowledge:
6 identify the compromises and trade-offs which must be made when translating theory into practice;
7 critically read and report on research papers.

Introductory material: Basic ideas of classification, regression and preliminary concepts. Data description. 

Statistical preliminaries: Probability, standard distributions and densities, Bayes rule.

Neural networks: common neural network architectures, MLPs, RBF networks.

Classification and receiver operating characteristic curves, KNN classifiers, linear discriminants, kernel-based classifiers.  

Learning and generalisation: maximum likelihood estimators, Bayesian learning,  optimisation in practice.  

 Unsupervised methods: clustering,  k-means, mixture models and the expectation-maximisation algorithm. 

Dimension reduction and visualisation: PCA, ICA, linear and nonlinear methods for visualisation, MDS and isomap.

Feature extraction: sequential forwards/backwards selection.

The initial workshop will be formatively assessed.  Other workshops will have formative components.

Reading list for this module: