Machine Learning in Econometrics

(4 Einträge)

Lecture Machine Learning in Econometrics, 1. Lesson

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Title: Lecture Machine Learning in Econometrics, 1. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 30. April 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-04-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210430_001_sose21mleco_0001
Rights: Rechtshinweise
Abstracts: 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.

Lecture Machine Learning in Econometrics, 2. Lesson

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Title: Lecture Machine Learning in Econometrics, 2. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 30. April 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-04-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210430_002_sose21mleco_0001
Rights: Rechtshinweise
Abstracts: 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.

Lecture Machine Learning in Econometrics, 3. Lesson

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Title: Lecture Machine Learning in Econometrics, 3. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 30. April 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-04-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210430_003_sose21mleco_0001
Rights: Rechtshinweise
Abstracts: 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.

Lecture Machine Learning in Econometrics, 4. Lesson

preview Play
Title: Lecture Machine Learning in Econometrics, 4. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 30. April 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-04-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210430_004_sose21mleco_0001
Rights: Rechtshinweise
Abstracts: 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.