Advanced Decision Making: Predictive Analytics & Machine Learning

Summary of module for applicants:

This module aims to develop a deep understanding of ways of making decisions that are based strongly on data and information. Particular focus will be on mathematical, statistical and algorithmic-based decision-making models using predictive analytics and machine learning. Various cases will be examined. The software environment will be predominantly open-source R.

Main topics of study:

• Models used in decision-making
• Mathematics and statistical foundations of decision-making
• Principles of algorithm-based models
• Use of predictive analytics and machine learning in decision-making
• Analysis of case studies
• Assessment of accuracy, propagation of uncertainty and probabilities of uncertain events
• Utility vs. cost benefit/effectiveness
• Maximisation of expected utility of models

Learning Outcomes for the module

• Digital Proficiency - Code = (DP)
• Industry Connections - Code = (IC)
• Emotional Intelligence Development - Code = (EID)
• Social Intelligence Development - Code = (SID)
• Physical Intelligence Development - Code = (PID)
• Cultural Intelligence Development - Code = (CID)
• Community Connections - Code = (CC)
• UEL Give-Back - Code = (UGB)

At the end of this module, students will be able to:

Knowledge

1 Have a deep understanding of mathematical, statistical and algorithm-based decision-making (IC)

Thinking skills

2 Design and implement decision-making models (DP, PID)

3 Assign probabilities to uncertain events; cost-benefits to possible consequences; and making decisions that maximize expected utility (IC, EID)

Subject-based practical skills

4 Use machine learning in R and other decision-support tools (DP)

5 Critically evaluate alternative decision models and their comparative accuracy (IC)

Skills for life and work (general skills)

6 Conduct real-world projects using machine learning and predictive analytics (DP, PID)

7 Critically evaluate and analyse data and the accuracy of models (DP, EID)

8 Able to communicate machine-learning projects through well-crafted reports (SID, CID)