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UFCFMJ 15 M

UFCFMJ-15-M Machine Learning and Predictive Analytics

Overview

This module introduces the theory and practice behind a range of supervised and unsupervised machine learning methods, together with software implementations and best practice in evaluation and performance measurement.

From a foundation in principles of machine learning, this module aims to equip students with a flexible toolkit of methods and techniques, culminating in a demonstration of their abilities to apply them appropriately and effectively to a problem of their own choice.

Objectives

  1. Synthesise evidence on the value of data as an asset for businesses to “mine” knowledge and “predict” trends
  2. Develop and evaluate predictive analytics approaches and techniques such as regression and random forest classifiers
  3. Apply problem solving skills necessary for identifying the organisational capacity needed to employ a predictive analytics solution

Curriculum

Indicative list of topics:

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 (100%)