Machine Learning - Einzelansicht

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 

• Breiman, L. (1996) Heuristics of instability and stabilization in model selection. Ann. Statist., 24, 2350–2383. 
• Hoerl, A. and Kennard, R. (1988) Ridge regression. In Encyclopedia of Statistical Sciences, vol. 8, pp. 129–136. New York: Wiley. 
• Flom, P. L. and Cassell, D. L. (2007): Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use. NESUG 2007. 
• Varian, H. (2014): Big Data: New Tricks for Econometrics. Journal of Economic Perspectives 28(2), pp. 3-28. 
• Giraud, C. (2014): Introduction to High-Dimensional Statistics, Monographs on Statistics & Applied Probability, Chapman & Hall CRC (mathematical foundations of high-dimensional statistics) 
• Jones, Z., and Linder, F. (2015): Exploratory Data Analysis using Random Forests. 
• Friedman, J., Hastie, T., and Tibshirani, R. (2008): The Elements of Statistical Learning (Downloadable on Tibshirani website) 
• James, G., Witten, D., Hastie, T., and R. Tibshirani, R. (2013): An Introduction to Statistical Learning with Applications in R. Springer. 
• Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B, 58, 267–288 

We will be covering the following topics:

• Statistics, econometrics and machine learning
• Draw contrasts with traditional approaches
• How to use machine learning methods for predicition?
• How to use machine learning tools in R?
• Tree-based methods in R
• Analyze regression-based methods in R
• Parametric methods
• How to conduct empirical research?
• How to write an empirical paper?

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.