Machine Learning in Econometrics

(44 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

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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.

Lecture Machine Learning in Econometrics, 5. Lesson

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Title: Lecture Machine Learning in Econometrics, 5. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 07. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-07
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210507_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, 6. Lesson

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Title: Lecture Machine Learning in Econometrics, 6. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 07. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-07
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210507_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, 7. Lesson

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Title: Lecture Machine Learning in Econometrics, 7. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 07. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-07
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210507_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, 8. Lesson

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Title: Lecture Machine Learning in Econometrics, 8. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 07. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-07
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210507_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.

Lecture Machine Learning in Econometrics, 9. Lesson

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Title: Lecture Machine Learning in Econometrics, 9. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 14. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-14
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210514_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, 10. Lesson

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Title: Lecture Machine Learning in Econometrics, 10. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 14. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-14
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210514_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, 11. Lesson

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Title: Lecture Machine Learning in Econometrics, 11. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 14. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-14
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210514_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, 12. Lesson

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Title: Lecture Machine Learning in Econometrics, 12. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 14. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-14
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210514_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.

Lecture Machine Learning in Econometrics, 13. Lesson

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Title: Lecture Machine Learning in Econometrics, 13. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 21. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-21
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210521_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, 14. Lesson

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Title: Lecture Machine Learning in Econometrics, 14. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 21. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-21
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210521_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, 15. Lesson

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Title: Lecture Machine Learning in Econometrics, 15. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 21. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-21
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210521_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, 16. Lesson

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Title: Lecture Machine Learning in Econometrics, 16. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 21. Mai 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-05-21
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210521_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.

Lecture Machine Learning in Econometrics, 17. Lesson

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Title: Lecture Machine Learning in Econometrics, 17. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 04. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-04
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210604_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, 18. Lesson

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Title: Lecture Machine Learning in Econometrics, 18. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 04. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-04
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210604_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, 19. Lesson

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Title: Lecture Machine Learning in Econometrics, 19. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 04. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-04
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210604_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, 20. Lesson

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Title: Lecture Machine Learning in Econometrics, 20. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 04. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-04
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210604_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.

Lecture Machine Learning in Econometrics, 21. Lesson

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Title: Lecture Machine Learning in Econometrics, 21. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 11. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-11
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210611_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, 22. Lesson

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Title: Lecture Machine Learning in Econometrics, 22. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 11. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-11
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210611_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, 23. Lesson

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Title: Lecture Machine Learning in Econometrics, 23. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 11. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-11
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210611_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, 24. Lesson

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Title: Lecture Machine Learning in Econometrics, 24. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 11. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-11
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210611_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.

Lecture Machine Learning in Econometrics, 25. Lesson

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Title: Lecture Machine Learning in Econometrics, 25. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 25. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-25
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210625_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, 26. Lesson

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Title: Lecture Machine Learning in Econometrics, 26. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 25. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-25
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210625_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, 27. Lesson

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Title: Lecture Machine Learning in Econometrics, 27. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 25. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-25
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210625_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, 28. Lesson

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Title: Lecture Machine Learning in Econometrics, 28. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 25. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-25
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210625_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.

Lecture Machine Learning in Econometrics, 29. Lesson

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Title: Lecture Machine Learning in Econometrics, 29. Lesson
Description: Vorlesung im SoSe 2021; Mittwoch, 30. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210630_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, 30. Lesson

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Title: Lecture Machine Learning in Econometrics, 30. Lesson
Description: Vorlesung im SoSe 2021; Mittwoch, 30. Juni 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-06-30
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210630_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, 31. Lesson

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Title: Lecture Machine Learning in Econometrics, 31. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 02. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-02
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210702_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, 32. Lesson

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Title: Lecture Machine Learning in Econometrics, 32. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 02. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-02
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210702_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, 33. Lesson

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Title: Lecture Machine Learning in Econometrics, 33. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 02. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-02
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210702_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, 34. Lesson

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Title: Lecture Machine Learning in Econometrics, 34. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 02. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-02
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210702_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.

Lecture Machine Learning in Econometrics, 35. Lesson

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Title: Lecture Machine Learning in Econometrics, 35. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 09. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-09
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210709_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, 36. Lesson

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Title: Lecture Machine Learning in Econometrics, 36. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 09. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-09
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210709_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, 37. Lesson

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Title: Lecture Machine Learning in Econometrics, 37. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 09. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-09
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210709_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, 38. Lesson

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Title: Lecture Machine Learning in Econometrics, 38. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 09. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-09
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210709_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.

Lecture Machine Learning in Econometrics, 39. Lesson

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Title: Lecture Machine Learning in Econometrics, 39. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 16. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-16
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210716_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, 40. Lesson

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Title: Lecture Machine Learning in Econometrics, 40. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 16. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-16
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210716_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, 41. Lesson

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Title: Lecture Machine Learning in Econometrics, 41. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 16. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-16
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210716_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, 42. Lesson

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Title: Lecture Machine Learning in Econometrics, 42. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 16. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-16
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210716_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.

Lecture Machine Learning in Econometrics, 43. Lesson

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Title: Lecture Machine Learning in Econometrics, 43. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 23. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-23
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210723_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, 44. Lesson

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Title: Lecture Machine Learning in Econometrics, 44. Lesson
Description: Vorlesung im SoSe 2021; Freitag, 23. Juli 2021
Creator: Jantje Sönksen (author)
Contributor: ZDV Universität Tübingen (producer)
Publisher: ZDV Universität Tübingen
Date Created: 2021-07-23
Subjects: Wirtschaftswissenschaft, Machine Learning, Econometrics,
Identifier: UT_20210723_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.