# Modules

### MAS8383 : Statistical Learning Methodology

• Offered for Year: 2019/20
• Module Leader(s): Dr Wentao Li
• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus
##### 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

##### Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture122:0024:00Lectures
Scheduled Learning And Teaching ActivitiesPractical122:0024:00Computer Practicals
Guided Independent StudyProject work132:0032:00Project
Guided Independent StudyProject work45:0020:00Coursework
Total100:00
##### Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. A large number of practicals are scheduled in order to provide sufficient hands-on training and rapid feedback on understanding.

#### Assessment Methods

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

##### Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M45Up to three practical reports. Word count: up to 1000 words as specified for each report
Report1M55Project report Word count up to 1500 words
##### Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral ExaminationMA structured discussion including a software demonstration and reflection on the key learning objectives of the coursework project
##### Assessment Rationale And Relationship

Written assignments (approximately 3 pieces of work of approximately equal weight) followed by a larger piece of project work allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the smaller pieces of work are thus formative as well as summative assessment.

The semi-structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of fundamental statistics are embedded in the functionality of their project work.