PULS
Foto: Matthias Friel
ECTS: 6 Credit Points
Venables, W. N., Smith, D. M. and the R Core Team (2018): An Introduction to R. https://cran.rproject.org/doc/manuals/r-release/R-intro.pdf
We will be covering the following topics:
No previous knowledge of machine learning is required since this is an introductory class. I expect that students have completed an undergraduate-level introduction to econometrics and statistics. The course requires basic knowledge of the OLS regression method. Prior experience with the software R is not a prerequisite, however, it is certainly advantageous.
Portfolioprüfung:
Oral exam (50%)Term paper (50%)
This course provides a broad introduction to microeconometric empirical methods for economists, including traditional econometric methods and machine learning techniques. The target audience are master students interested in learning how to perform data analysis and solve prediction problems. Students will learn how to use the statistical software R. Completing the course will enable students to conduct independent empirical research in their master thesis as well as future jobs (e.g. public policy institutions, consulting firms, and doctoral programs).
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: this course aims to make ML 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 linear regression and tree-based methods. In addition, I will show R codes to familiarize with the algorithms’ implementation. Existing statistical packages make it trivial to do ML in practice. However, I will show how economic intuition still plays a crucial role in improving the algorithms’ performance. At the end of the course, students will know how to use ML methods to solve problems that traditional econometrics cannot.
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