Updated A.Y. 2022-2023
The course covers some statistical techniques for supervised and unsupervised learning. The R software for statistical computing will be also introduced and used throughout.
Supervised learning techniques are used to predict a target variable (linear and logistic regression) based on predictors, and/or to assess interrelationships among predictors and a target variable (linear and logistic regression). As an example, suppose you want to predict the risk that a family will be materially deprived next year. This can be done by using data that can be measured at baseline (number of family members, disposable income, health status, etc.) and use these to predict material deprivation for a sample of families with known status. Incidentally, you will also understand how health status affects the risk of material deprivation.
Find more information in the Syllabus