# MAS8383 : Statistical Learning Methodology (Inactive)

• Inactive for Year: 2024/25
• Module Leader(s): Dr Clement Lee
• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus
##### Semesters

Your programme is made up of credits, the total differs on programme to programme.

 Semester 1 Credit Value: 10 ECTS Credits: 5.0 European Credit Transfer System

#### 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

##### Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion112:0012:00Formative exercise
Scheduled Learning And Teaching ActivitiesLecture62:0012:00Present in person lectures
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in person structured synchronous practical
Guided Independent StudyProject work148:0048:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery41:004:00Present in person drop-in
Total100:00
##### Teaching Rationale And Relationship

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 are two present-in-person practical sessions per week to ensure rapid feedback on understanding. Scheduled present-in-person drop-ins provides opportunity for students to ask questions and receive immediate feedback.

#### Assessment Methods

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

##### Other Assessment
Description Semester When Set Percentage Comment
Report1M100Main 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

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

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.