Participation in the course requires prior application. The initial application deadline has passed but there are some seats available. If you are interested in taking the class, please apply by sending your current transcript of records to jantje.soenksenspam prevention@uni-tuebingen.de no later than April 12, 2018. Applicants will be informed on the outcome of their application on April 13, 2018.

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

Practical classes on April 19th an 20th deal with a general introduction to Matlab.

Starting week 3, the practical classes will deal with Machine Learning topics.

All students are expected to be present during these sessions.

will be announced in class