A variety of topics are introduced, including basic ideas of statistical learning (supervised versus unsupervised, regression versus classification, model accuracy assessment), and some key concepts, models and methods of principle components analysis, decision trees as well as cluster analysis. All models and methods are illustrated with extensive examples from business and management [3 credits]
A variety of topics are introduced, including basic ideas of statistical learning (supervised versus unsupervised, regression versus classification, model accuracy assessment), and some key concepts, models and methods of principle components analysis, decision trees as well as cluster analysis. All models and methods are illustrated with extensive examples from business and management [3 credits]
