This module illustrates how machine learning techniques can be used in economic research and applications. It offers a thorough analysis of various tools in statistical learning and links them to econometric analysis. The course focuses on supervised machine learning techniques, such as: decision/regression trees, (logistic) regressions, naïve Bayes, local regressions, nearest neighbors, artificial neural networks, and support vector machines. The lecture also covers hyper-parameter tuning methods and different feature selection and regularization techniques. A practical PC-Lab class is an essential part of the module.
