Machine Learning Crash Course, Tallinn, August 18-19

The course provides an introduction to machine learning methods. The focus of the lecture is on supervised machine learning methods, including penalized regression and tree-based methods. Furthermore, we discuss unsupervised machine learning methods (clustering and principal component analysis). The lectures are accompanied by coding sessions in which the machine learning methods are applied to real-life economic and business problems (using the open source software R).

Learning objectives / competences: 1) Course participants will be familiar with the principles of prediction. 2) Course participants will be able to distinguish between supervised and unsupervised machine learning methods. 3) Course participants will deploy machine learning methods to economic and business prediction problems.

Day 1:

• Principals of prediction
• Penalized regression methods

Day 2:

• Trees and random forests
• Unsupervised machine learning