[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process

[pdf] [www]

Note: malitup

Abstract: The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process