This course introduces data mining and machine learning methods used in big data scenarios and also introduces methods for analysing networks and unstructured data.

The course mostly consists of asynchronous teaching material.

Big Data Analytics

Big Data Analytics (Level M)

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100% Continuous Assessment

This will typically be made up of a project (40%),presentation (20%) and two homework exercises / online quiz (40%). Full details are provided in the programme handbook.

The aims of this course are:

■ to introduce students to network analysis including social network analysis and knowledge graphs

■ to introduce the students to big data methods commonly applied in Machine Learning, notably regularised regression;

■ to illustrate the role of sparsity when analysing high-dimensional data;

■ to introduce students to graphical models and how they can be used for structural inference in high-dimensional data;

■ to introduce students to informal and formal methods for quantitative text analysis.

By the end of this course students will be able to:

■ describe the challenges of the analysis of high-dimensional data and discuss, in a particular context, strategies for tackling big data problems;

■ formulate and fit a regularised linear model, such as ridge regression, the LASSO and partial least-squares;

■ infer statements about (conditional) independence from graphical models and factorisations of the joint distribution;

■ describe methods for structural inference in graphical models and apply them in a given context;

■ make appropriate use of informal and formal methods for network analysis and quantitative text analysis.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.