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Lecture Machine Learning in Econometrics, 13. Lesson
Sönksen, Jantje (2021)
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mla
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Sönksen J. "Lecture Machine Learning in Econometrics, 13. Lesson.", timms video, Universität Tübingen (2021): https://timms.uni-tuebingen.de:443/tp/UT_20210521_001_sose21mleco_0001. Accessed 24 Jul 2021.
apa
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Sönksen, J. (2021). Lecture Machine Learning in Econometrics, 13. Lesson. timms video: Universität Tübingen. Retrieved July 24, 2021 from the World Wide Web https://timms.uni-tuebingen.de:443/tp/UT_20210521_001_sose21mleco_0001
harvard
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Sönksen, J. (2021). Lecture Machine Learning in Econometrics, 13. Lesson [Online video]. 21 May. Available at: https://timms.uni-tuebingen.de:443/tp/UT_20210521_001_sose21mleco_0001 (Accessed: 24 July 2021).
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title: Lecture Machine Learning in Econometrics, 13. Lesson
alt. title:
creator: Sönksen, Jantje (author)
subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics, Lecture
description: Vorlesung im SoSe 2021; Freitag, 21. Mai 2021
abstract: This module illustrates how machine learning techniques can be exploited in economic research and applications. It offers a thorough analysis of a variety of tools in machine learning and links them to econometric analysis. The class focuses on supervised machine learning algorithms such as: decision trees, (logistic) regressions, naïve Bayes, nearest neighbor, neural networks, and support vector machines. The lecture also covers feature selection and hyper-parameter tuning methods. A practical PC-Lab class is an essential part of the module. Students apply state-of-the-art machine learning techniques and understand how these are linked to standard econometrics. They command different machine learning methods and apply them to economic problems. They are aware of the respective advantages and shortcomings of these methods and discuss their results critically.
publisher: ZDV Universität Tübingen
contributor: ZDV Universität Tübingen (producer)
creation date: 2021-05-21
dc type: image
localtype: video
identifier: UT_20210521_001_sose21mleco_0001
language: eng
rights: Url: https://timmsstatic.uni-tuebingen.de/jtimms/TimmsDisclaimer.html?637626899746711096