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

Professional course

Machine Learning and Predictive Analytics

About this course

Course code:
Z41000092
Applications:
University
Level:
Professional/Short Course
Department:
Computer Science and Creative Technologies
Campus:
Frenchay
Duration:
Twelve two-hour sessions, scheduled over twelve continuous Wednesdays
Delivery:
Through weekly lectures and tutorial sessions
Programme leader:
Dr Hisham Ihshaish
Key fact:
This course can count towards one of our postgraduate qualifications within our Information Management and Information Technology Awards.

Page last updated 4 September 2019

Introduction

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

Participants are expected to have a first degree at 2.2 level or above (or equivalent), or alternatively have six-months of relevant industrial experience.

Overseas students need to have, in advance of application, either a valid student visa or evidence of residency status in the UK.

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 standalone module, or used to build up credit towards a named postgraduate qualification (PG Certificate, PG Diploma or Masters) within our Information Management and Information Technology awards

Structure

Content

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 lectures and tutorial sessions, which take place on consecutive weeks.

Each lecture will direct the course and introduce the new ideas and skills required. Then small group tutorial sessions will enable each student to carry out the study and research exercises described in the associated work-sheet under the guidance of a Tutor.

The teaching material is available from Blackboard (our online learning environment).

A course text is also recommended.

Study time

This module will involve 2 hours direct contact time per week for one semester equally divided between lecture and tutorial sessions.

Each 15 credit course (module) is expected to take 150 hours to complete:

  • 24 hrs contact time trough lectures and face to face discussion
  • 30 hrs coursework preparation
  • 86 hrs assimilation and development of knowledge
  • 10 hrs exam preparation

Assessment

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

For more details, see our full glossary of assessment terms.

Features

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

CohortJanuary 2020
UK/EU Participants£625
International Participants£1,083
Application DeadlineMonday 13 January 2020*

*Please note parking and refreshments are not included as part of your course fee

Course dates

 
CohortStart DateSession Time
January 2020Wednesday 22 January 2020*14:30 - 16:30

*then every Wednesday until 01 April 2020; and 22 April 2020

Location

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

How to apply

How to apply

As this module carries university accreditation, please note that once you have submitted your booking form, you will be required to provide the following supporting information to the administration team for the module tutor to be able to review and formally accept your application as required by the university:

  • An up-to-date copy of your Curriculum Vitae (including contact details of a work or academic reference)
  • A brief personal statement to support your application
  • A copy of your highest qualification certificate and transcript of modules studied
  • A copy of photographic proof of ID (i.e. driver's licence/passport). For non UK students, this must be a copy of your passport

Overseas participants will be required to provide evidence of visa status once accepted onto the course, before they can be fully registered and attend.

Cohort  
January 2020Book NowEnquire Now

For further information

  • Email: For all queries regarding administration aspects of registration, i.e. dates, fees, etc. please contact us using the online enquiry form link or telephone number below. For any questions in relation to the course, i.e. content, suitability, assessments, etc. please contact the Programme Leader, Dr Hisham Ihshaish.
  • Telephone: +44 (0)117 32 86927

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