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:
Learning Outcomes for the module
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)