A student and lecturer looking at a bank of monitors, analysing figures

Professional course

Machine Learning and Predictive Analytics

About this course

Course code:
Professional/Short Course
Computer Science and Creative Technologies
Twelve two hour sessions scheduled over a period of 12 weeks
Course director:
Dr Hisham Ihshaish

Page last updated 28 February 2019


This module will help you to understand how organisations can capitalise on their information assets and use them for both evaluating performance and identifying new opportunities. Key principles and approaches to machine learning will be covered alongside examples of how they relate to business problems and research/development questions.

Entry requirements

You are expected to have a first degree at 2.2 level or above (or equivalent), or alternatively have industrial experience. We strongly recommend that you speak to the course tutor prior to the course if you are unsure about your suitability to complete the assessment.

Careers / Further study

This module can be taken as a stand alone module, or used to build up credit towards a named postgraduate qualification (PG Certificate, PG Diploma or Masters) within our Information Technology Award.



This module will cover the following topic areas:

Introduction to predictive analytics

  • Defining predictive analytics - introduction
  • Business Relevance of PA - Business intelligence and applications
  • Relevance of pattern recognition, classification, optimisation
  • Predictive analytics and big data
  • Case study: A business application using predictive analytics approaches 

Predictive analytics in business - applications

  • Sources of data and value of knowledge
  • Identify a wide range of applications for predictive analytics:
  • Marketing and recommender systems, fraud detection, business process analytics, credit risk modelling, web analytics and others
  • Social media and human behaviour analytics
  • Case study: Email targeting - which message will a customer answer? - [tutorial]

Analytics models and techniques

  • Introduction to analytics modelling
  • Types of analytics models:
  • Predictive models
  • Survival models
  • Descriptive models
  • Define pattern recognition, inferring data and data visualisation
  • Briefing learning and regression approaches
  • Comparison of approaches - use and goals - [tutorial]

Introduction to machine learning

  • Introduction: Basic principles
  • Basic notions of learning
  • Introduction to learning problems (classification, clustering and reinforcement) and literature
  • Identifying different learning approaches - supervised, unsupervised and reinforcement
  • Case study on different types of learning - [tutorial]

Machine learning for predictive analytics [1]

  • Review of types of problems
  • Machine Learning techniques:
  • Decision tree learning
  • Artificial neural networks
  • Clustering
  • Naive Bayes classifier
  • k-nearest neighbours
  • Genetic algorithms
  • Case study on problem - a "suitable" predictive modelling technique - [tutorial]

Regression techniques for predictive analytics

  • Review of types of problems (application)
  • Linear regression models
  • Survival or duration analysis (time to event analysis)
  • Ensemble learning and random forest
  • Case study on problem - a "suitable" predictive modelling technique - [tutorial]

 Advanced topics and Software tools

  • Analytics in the context of big data
  • Predictive analytics as art and science
  • Software tools; the R project and Python
  • Trends and challenges in predictive analytics - where are we going?

Learning and Teaching

The module is delivered through weekly combined lecture and tutorial sessions. Each session will direct the course and introduce the new ideas and skills required. Then tutorial sessions will enable you to carry out the study and research exercises described in the associated work-sheet under the guidance of a Tutor.

The teaching material will be made available from Blackboard. A course text is also recommended.

Scheduled learning includes lectures and tutorials.  

Independent learning includes time engaged with essential reading and assignment preparation and completion.


The module will be assessed though a coursework project and a written exam.


Study facilities

The University has excellent facilities, accessible to all students, as required; however, it is expected that much of the work will be carried within the work environment.

Find out more about the facilities and resources UWE has to offer.

Prices and dates

Supplementary fee information

CohortUK/EU ParticipantsInternational Participants
January 2019£583£1,042

Course dates

This course will next run in our 2019-20 academic year. To be kept up-to-date with our course dates please complete the enquiry form below.


UWE Bristol, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY

How to find UWE Bristol

How to apply

How to apply

January 2020Enquire Now

For further information

  • Email: For all queries, please complete the online enquiry form above.
  • Telephone: +44 (0)117 32 86927

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