Machine Learning Methods and Applications in Economics

The thesis introduces supervised and unsupervised learning concepts having in mind an unexperienced audience, pointing out relevant references for further studies. Moreover, we highlight the relevance of Machine Learning for Economics and what are the possible applications. Then, the work proceeds with two contributions. The first one is a methodological contribution to cluster analysis; here we propose a novel method to score and evaluate clustering solutions where clusters are parametrized by centres, scatters and sizes parameters. The second contribution is an application of Machine Lerning methods to Labor Economics. We explore the assignment of employees-to-tasks and use trees-based learning algorithms to retrieve a mapping for the assignment. We show that the so-derived assignment rule helps explaining productivity drivers.