Description:

The syllabus includes: statistical and machine learning methods, in particular: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This course deals with the topic of 'Statistical Learning' and is offered as a three-day block course, followed eventually by weekly tutorial sessions.

Outline:

• Linear regression and k-nearest neighbors
• Classification
• Resampling methods
• Linear Model selection and regularization
• Polynomial regression, splines and local regression
• Tree-Based methods
• Unsupervised learning

Literature:

• Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
• Davidson, R.; MacKinnon, J. G. (2004). Econometric theory and methods. New York: Oxford Univ. Press.
• Hastie, T.; Tibshirani R.; Friedman, J. (2013). The elements of statistical learning : data mining, inference, and prediction (2nd edition). New York: Springer.
• Hayashi, F. (2000). Econometrics. Princeton: Princeton Univ. Press.
• James, G.; Witten, D.; Hastie, T.; Tibshirani, R. (2016). An introduction to statistical learning : with applications in R. New York: Springer.
• Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd edition). Cambridge, Mass.: MIT Press. 

Formalities:

Successful participation in the module “Methoden der Ökonometrie” is recommended. Knowledge of basic econometric concepts such as communicated in our bachelor and master courses “Einführung in die Ökonometrie" and “Methoden der Ökonometrie“ as well as good working knowledge of mathematical statistics.

Links:

Information on credit eligibility and the course content can be found in the Modulhandbuch.