123241-44 | A Advanced Methods | Applied Machine Learning

Lehrende: Prof. Dr. Winfried Pohlmeier

Veranstaltungsart: Seminar / Übung

Orga-Einheit: Corporate Management & Economics

Anzeige im Stundenplan: Adv. Meth.

Semesterwochenstunden: 1,5

Credits: 3,0

Standort: Campus der Zeppelin Universität

Unterrichtssprache: Englisch

Min. | Max. Teilnehmerzahl: 10 | 35

Inhalte:
The course provides an introduction to major machine learning tools like shrinkage methods (ridge, lasso and elastic net), tree-based methods,
pricipla component analysis and artificial neuronal nets. Students will learn how to select variables and model specifications and to evaluate their predictive power.
 
Besides a good understanding of the theoretical foundations and their strengths and limitations, students will learn to apply the ML tools for economic problems by using R and Quarto.

 

Lernziele:
This course aims at endowing students with the basic competences to understand and pursue empirical research based on modern machine learning (ML) techniques for typical cross-sectional and time series data used in economics and finance.

Weitere Informationen zu den Prüfungsleistungen:
Besides the final examination (80% of the final grade) students will work on a case study (20%) where an application of a ML tool is presented. The results will be presented based on a Quarto code book.

Literatur:

• Chan, F. & L. Mátyás, L. (eds.) (2022): Econometrics with Machine Learning, Springer International Publishing.
  
• James, G., D. Witten, T. Hastie & R. Tibshirani (2021): An Introduction to Statistical Learning: With Applications in R, 2nd. edition, Springer, NY.
  

Modulbeschreibung: