UFCFMJ 15 M

UFCFMJ-15-M Machine Learning and Predictive Analytics

Overview

A practical introduction to a range of machine learning approaches and algorithms, together with problem definition and evaluation.

Objecives

1. Define and critique through example the concepts of predictive analytics, machine learning and data mining
2. Differentiate analytical models: the predictive, descriptive and survival
3. Synthesise evidence on the value of data as an asset for businesses to “mine” knowledge and “predict” trends
4. Identify learning problems including classification, clustering and reinforcement; distinguish their scope and outline suitable solutions
5. Develop and evaluate predictive analytics approaches and techniques such as regression and random forest classifiers
6. Apply problem solving skills necessary for identifying the organisational capacity needed to employ a predictive analytics solution
7. Visualise and present the results of predictive and descriptive models alongside an evaluation of performance and recommendations for improvement
8. Understand predictive analytics trends and challenges and illustrate fluency with software tools used in predictive analytics

Curriculum

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?

Assessment

Report (50%)

Exam (50%)