Postgraduate

Modules

Modules

MAS8383 : Statistical Learning Methodology

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

More data than ever before are being generated and stored, in a variety of fields across industry. The term "big data" has emerged in acknowledgement of the vast amounts of data now available. By applying statistical analyses to these data sets, we can start to use them to answer important questions such as (i) which are the important factors affecting the quality of an industrial process; (ii) how many different types of customer are interested in your product. Commonly the data sets that arise in industry are multivariate, comprising a large number of observations on many variables. In this module we study how we can learn from data sets of this form. Attention is paid to the statistical and mathematical theory underpinning the methods, in addition to their practical application using R.

Specifically, the module aims to equip students with the following knowledge and skills:

- To gain an understanding of the theory behind modern statistical approaches to learning from data.
- To gain experience in the application of these techniques to the analysis of large and complex data sets across a range of application areas.

Outline Of Syllabus

- Fundamentals of multivariate data analysis, including data representation, summary and core applications of linear algebra
- Theory and practice of principal components analysis
- Cluster analysis
- Linear regression, including variable selection, regularisation (ridge regression, the lasso, the elastic net) and dimension-reduction techniques
- Classification, including discriminant analysis and logistic regression
- Generalized linear models
- Tree-based methods, including regression trees, classification trees and random forests

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials32:307:30Structured non-synchronous practical
Structured Guided LearningLecture materials91:3013:30Non-synchronous online pre-recorded lectures and set reading
Guided Independent StudyAssessment preparation and completion214:0028:00Formative and summative reports
Scheduled Learning And Teaching ActivitiesPractical31:304:30Present in person structured synchronous practical
Guided Independent StudyProject work128:3028:30Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery31:304:30Virtual office hour, synchronous on line
Guided Independent StudyIndependent study91:3013:30Lecture follow up/background reading
Total100:00
Teaching Rationale And Relationship

Pre-recorded lectures and set reading are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. There is one present-in-person practical session per week to ensure rapid feedback on understanding, and further non-synchronous practical sessions to provide additional practice and experience. Scheduled on-line contact time provides opportunity to ask questions and receive immediate feedback. Students unable to attend PiP will be able to complete the practical work at home and will be able to receive immediate feedback through the scheduled on-line contact time.

Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.
Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M40Individual report
Report1M60Main module project
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA 3 min video articulating the main findings of one aspect of the coursework report
Formative Assessments
Description Semester When Set Comment
Practical/lab report1MA compulsory report allowing students to develop problem solving techniques, to practise the methods learnt and to assess progress.
Assessment Rationale And Relationship

A compulsory formative practical report allows the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessments.

The oral presentation encourages students to focus on interpretation of statistical results, builds their skills in the presentation of statistical concepts, and provides opportunity for feedback.

In a foundational subject like the Mathematical Sciences, there is research evidence to suggest that continual consolidation of learning is essential and the fewer pieces of assessment there are, the more difficult it is to facilitate this. On this module, it is particularly important that the material on the earlier summative assessment is fully consolidated, before the later assessment is attempted.

Reading Lists

Timetable