Marica Valente, PhD student at the Berlin School of Economics, will give a course on Machine Learning Methods for Prediction and Treatment Effect Estimation. The course starts on Wednesday, 10/6/2020, 3pm until 5pm. On Thursday and Friday, the course starts at 9:30 am and ends at 4:30 pm.
Overview of class purpose and content
Machine learning (ML) defines a set of modern empirical tools used in fields like statistics, computer science, AI and, more recently, economics. ML in economics is often viewed as a black-box, and this course aims to make such a box less obscure and more accessible. In this course, we will walk through the basics of ML with a focus on supervised learning such as regularized regression and tree-based methods for both prediction and causal effect estimation. At the end of the course you will know how to use ML methods to solve problems that standard econometrics cannot. In addition, there will be two R sessions to familiarize with the algorithms’ implementation. Existing statistical packages make it trivial to do ML in practice. However, we will show how economic intuition still plays a crucial role in improving the algorithms’ performance.
Requirements
No previous knowledge of ML is required since this is an introductory class. The course requires some basic knowledge of econometrics, and R coding. Please make sure to have RStudio installed before the beginning of the class.
For further details, please consider the syllabus. Please sign up for the course via e-mail to astrid.pape@fu-berlin.de and note that the number of slots is restricted.