ST3MML: Methods of Machine Learning

Module code: ST3MML

Module provider: Mathematics and Statistics; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 3 (Honours)

When you'll be taught: Semester 1

Module convenor: Dr Fazil Baksh, email: m.f.baksh@reading.ac.uk

Module co-convenor: Dr Julia Abery, email: j.abery@reading.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST ( TAKE MA1LA OR TAKE MA1LANU ) AND ( TAKE ST1PS OR TAKE ST1PSNU OR TAKE EC120 ) (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 21 May 2024

Overview

Module aims and purpose

The topics of Data Science, Machine Learning and Artificial Intelligence are now part of the public consciousness, in part due to their successful application in industry. Many of the most successful techniques used in these fields are underpinned by statistical techniques. The aim of this module is to introduce students to a range of methods currently used in statistical machine learning, and to demonstrate how these are used in research and industry. The module begins by considering the application of unsupervised machine learning in exploratory multivariate data analysis and then moves on to consider methods of supervised machine learning in regression and classification. As well as being instructed in the theory underlying these methods, students will be given opportunity to implement machine learning methods using statistical software and then to interpret and communicate their findings. 

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Use and explain a range of statistical methods used in Machine Learning, and recognise which to use in different situations
2. Produce software implementation of the methods taught in the module and interpret findings
3. Use statistical learning tools to build and evaluate algorithms for supervised learning

Module content

The module begins by considering the application of unsupervised machine learning in exploratory multivariate data analysis, covering the topics of data visualisation, principal component analysis, canonical variates analysis, cluster analysis and factor analysis. The module then discussessupervised machine learning, covering the topics of regression and classification, including: linear and logistic regression;linear and quadratic discriminant analysis; resampling methods; model selection and regularisation; ridge regression; lasso; dimension reduction methods; principal components regression; partial least squares; high dimensional problems; regression splines; generalised additive models; tree-based methods; bagging; stacking; random forests; boosting; neural networks and deep learning; support vector machines. 

Structure

Teaching and learning methods

The core material is delivered via lectures. These are supported by tutorials in which students work through non-assessed exercises and computer classes in which students practise the methods. 

Study hours

At least 55 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Non assessed problem sheets and computer practicals.

Reassessment

Additional costs