Lecture

Statistical Learning

Name in diploma supplement

Statistical Learning

Organisational Unit

Lehrstuhl für Ökonometrie

Lecturers

Dr. Thomas Deckers

Cycle

summer semester

SPW

2

Language

English

Participants at most

no limit

Participants

• BWL EaF MasterWahlpflichtbereich 1.-3. Sem, Elective, Module Statistical Learning
• ECMX MasterWahlpflichtbereichME7 Econometric Methods 1.-3. Sem, Elective, Module Statistical Learning
• ECMX MasterWahlpflichtbereichME6 Applied Econometrics 1.-3. Sem, Elective, Module Statistical Learning
• VWL MasterWahlpflichtbereich I 1.-3. Sem, Elective, Module Statistical Learning
• GOEMIK MasterWahlpflichtbereich Bereich Volkswirtschaftslehre 1.-3. Sem, Elective, Module Statistical Learning
• MuU MasterWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen 1.-2. Sem, Elective, Module Statistical Learning
• WiInf MasterWahlpflichtbereichWahlpflichtbereich II: Informatik, BWL, VWLWahlpflichtmodule der Volkswirtschaftslehre 1.-3. Sem, Elective, Module Statistical Learning

Preliminary knowledge

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.

Contents

• Linear regression and k-nearest neighbors
• Classification
• Resampling methods
• Linear Model selection and regularization
• Polynomial regression, splines and local regression
• Tree-Based methods
• Support vector machines
• 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.

Teaching concept

Classes are organized around traditional lectures. Students are however expected to contribute intensively through active discussion. Lectures are complemeted via, e.g., illustrations in R, joint interactive programming to better understand the statistical concepts as well as comprehensive problem sets to deepen students’ proficiency.