Professional course Machine Learning and Predictive Analytics

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

A 15 credit module, like this, is expected to take 150 hours to complete:

• 24 hrs contact time through 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.