Module code: MANM354

Module Overview

Machine Learning & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification. 

Module provider

Surrey Business School

Module Leader

FU Colin (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 90

Lecture Hours: 11

Laboratory Hours: 22

Guided Learning: 12

Captured Content: 15

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

The module content will focus on a selected set of critical areas in Machine Learning. As an indication of the kind of issues that will be covered, please find below an indicative set of topics.

  • Statistical Learning

  • Classification (e.g. Logistic Regression)

  • Tree base methods  (e.g. random forest, decision trees)

  • Support Vector Machines

  • Unsupervised Learning (e.g. k-means clustering)

  • Meta Heuristics (e.g. genetic algorithms, ANN, deep learning)

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 100

Alternative Assessment


Assessment Strategy

In order to achieve the threshold standard for the award of credits for this module, the student must meet the following criteria related to the learning outcomes:

  1. apply the theories, conceptual frameworks and methodologies that underpin machine learning;

  2. prove the ability to visualise data and business insights;

  3. demonstrate evidence of background reading and research of the academic and practitioner literature relevant to Machine Learning.

The summative assessment for this module is a piece of investigative coursework.


Students will apply and deepen their knowledge with the criteria related to learning outcomes (1) to (3) mentioned above. Student(s) must apply business analytics to a chosen business problem which the topic(s) will be given in the beginning of the semester.


In the coursework, business context must be provided with analysis of data. The analysis will use machine learning techniques, creating algorithms and visualising the results appropriately. Solutions and recommendations will need to be given with rationale. Furthermore, evidence of background reading has to be provided.


Formative assessment will be provided throughout the course. Students will demonstrate the ability to analyse and visualise business. They will be given business problems on a weekly basis. Machine Learning techniques will be used for gaining business insights. Formative feedback on the student’s approach will be given during the computer laboratory sessions.

Module aims

  • To gain business insights using Machine Learning and Visualisations

Learning outcomes

Attributes Developed
001 Analyse, classify and visualise data; KPT
002 Explain and apply supervised & unsupervised learning; CPT
003 Give insights into business analytic challenges; CPT
004 Present results to management for decision making. CPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The teaching and learning strategy is designed to: cultivate an understanding of the main issues and challenges provide a coherent conceptual framework develop a critical awareness of the various approaches of machine learning.


The teaching and learning methods include: a lecture, laboratory session, captured contents and guided learning every week as well as take home activities. Web-based learning support and electronic resources will also be provided.


The module will develop analytical skills and the understanding of the subject area through:

  • Lectures

  • In-class discussion

  • Independent learning

  • Guided learning

  • Captured content

The module will develop practical skills through:

  • Lab sessions

  • Coursework

  • Peer discussion

  • Independent learning

  • Guided learning

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: MANM354

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Analytics MSc 2 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.