Applied Machine Learning in Economics and Finance

Update 31.07.2023: The exam review takes place on Tuesday, October 17 from 2-3 p.m. in room 01 020, 1stfloor, Rempartstr. 16. floor. Prior registration on Ilias is necessary. You can register until 09.10.2023 at 13:30.

The teaching of this course will begin on May 22, 2023. There will be no livestreaming of the lectures or exercise sessions.

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Registration for the Lecture	Students have to sign in for this course in HISinOne. The registration in ILIAS will be carried out automatically.
Registration for the Exam	Please note that the registration for the lecture does not automatically mean that you are registered for the exam! A separate registration for the exam is mandatory! You can find the current examination dates as well as further information on the registration for the examination as well as the deadlines for registration and deregistration of the examinations on the homepage of the examination office.
ILIAS	Course material can be downloaded from ILIAS. For further material, updates, and relevant information, please keep checking ILIAS. The access password will be announced during the first lecture.
Instructor	Prof. Dr. Winfried Pohlmeier
Lectures	22.05.2023: 14:00-16:00 c.t., HS 3044, KG III 22.05.2023: 16:00-18:00 c.t., HS 3042, KG III 23.05.2023: 08:00-10:00 c.t., HS 1009, KG I 23.05.2023: 10:00-12:00 c.t., HS 1108, KG I 12.06.2023: 14:00-16:00 c.t., HS 3044, KG III 12.06.2023: 16:00-18:00 c.t., HS 3042, KG III 13.06.2023: 08:00-10:00 c.t., HS 1009, KG I 13.06.2023: 10:00-12:00 c.t., Raum 01 014, Wilhelmstr. 26 03.07.2023: 14:00-16:00 c.t., HS 3044, KG III 03.07.2023: 16:00-18:00 c.t., HS 3042, KG III 04.07.2023: 08:00-10:00 c.t., HS 1009, KG I 04.07.2023: 10:00-12:00 c.t., Raum 01 014, Wilhelmstr. 26 04.07.2023: 14:00-16:00 c.t., HS 1221, KG I
Language	English
Credits	4 ECTS
Requirements	Statistics, Econometrics
Qualification Target	This course aims at endowing students with the basic competences to understand and pursue empirical research based on machine learning (ML) techniques for typical cross-sectional and time series data used in economics and finance.
Contents	The course covers the fundamentals of model selection for high dimensional data. The students learn to distinguish between predictive and causal modelling and the appropriate choice of the tuning parameters. Special emphasis will be given to interpretable machine learning techniques and modern causal machine learning methods. Besides a good understanding of the theoretical foundations and their strengths and limitations, students learn to practically apply the ML tools for economic problems by using R or Python.
Main References	Chan, F. & L. Mátyás, L. (eds.) (2022): Econometrics with Machine Learning, Springer International Publishing. eBook James, G., D. Witten, T. Hastie & R. Tibshirani (2021): An Introduction to Statistical Learning: With Applications in R, 2^nd edition, Springer, NY. eBook Hansen, B. E. (2022): Econometrics, Princeton University Press, Princeton. Nagel, S. (2021): Machine Learning in Asset Pricing, Princeton University Press, Princeton. VPN access is required for the eBooks.
Grading	100% final written exam (90 minutes)
Exam	Date and starting time: Wednesday, July 26, 2023, 12 a.m., Seminar room 01012, Rempartstr. 16, 1. OG A separate registration is mandatory for the exam! Period for registration and deregistration: 05.06.2023-05.07.2023 Please find further details on the examination office's homepage. The exam will be in presence. In the exam you may use a non-programmable calculator, a hard copy of a German-English dictionary book and a one-sided A4 hand-written cheat sheet.
Retake Exam	Date and starting time: Friday, February 9, 2024, 4 p.m., HS 3042 A separate registration is mandatory for the exam! Period for registration and deregistration: 18.12.23 - 18.01.24 Please find further details on the examination office's homepage. The exam will be in presence. In the exam you may use a non-programmable calculator, a hard copy of a German-English dictionary book and a one-sided A4 hand-written cheat sheet.
Target Group	The course can be chosen as an elective 4 ECTS course in: M.Sc. VWL (Accounting, Finance and Taxation / Business Analytics / Constitutional Economics and Competition Policy / Empirical Economics / Network Economics and IT Risk Management) M.Sc. BWL (Volkswirtschaftslehre / Quantitative Methoden / Wirtschaftsinformatik) M.Sc. in Economics (Economics and Politics / Finance / Information Systems and Network Economics)