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… it will be drafted in partnership with experts from all faculties and support units, making optimal use of the existing expertise at UM. Faculties will be asked to specify their own working methods in the context of the general guidelines, and to exchange knowledge and good practices with other faculties and service centres. Thus, the present policy framework will continue to evolve. The forthcoming elaborations will be widely disseminated within the university, either as appendices to this … for data retention, access, security and—importantly— protocols to follow in the event of a data breach. Furthermore, UM would do well to make agreements with providers about the careful processing of personal data whenever such data will be exchanged, even if processing of personal data is not the main purpose of the use of GenAI in the context of this partnership. 3. Retention of and access to data The GDPR requires personal data to be kept for no longer than is necessary for the purpose …

… it will be drafted in partnership with experts from all faculties and support units, making optimal use of the existing expertise at UM. Faculties will be asked to specify their own working methods in the context of the general guidelines, and to exchange knowledge and good practices with other faculties and service centres. Thus, the present policy framework will continue to evolve. The forthcoming elaborations will be widely disseminated within the university, either as appendices to this … for data retention, access, security and—importantly— protocols to follow in the event of a data breach. Furthermore, UM would do well to make agreements with providers about the careful processing of personal data whenever such data will be exchanged, even if processing of personal data is not the main purpose of the use of GenAI in the context of this partnership. 3. Retention of and access to data The GDPR requires personal data to be kept for no longer than is necessary for the purpose …

… hypothesis-driven versus data-driven research. The so-called “data science lifecycle” is used to guide students through the different steps of conducting a data-driven approach. Students learn about data, standard data types, formats and their exchange. In this module, students also trained about data privacy and protection (using methods for anonymization and pseudonymisation). Course objectives The specific course objectives are: Expert: The student is able to: know the historical origin … data standards in Python set up data science experiments use visualisations for data science storytelling clean and manipulate datause privacy-preserving techniques and data encryption in practice Recommended reading This is the link to Keylinks, our online reference list. HDT4001 Period 1 2 Sep 2024 25 Oct 2024 Print course description ECTS credits: 3.0 Instruction language: English Coordinator: V. Urovi Teaching methods: Assignment(s), Lecture(s), Work in subgroups, Presentation(s) Assessment … advanced algorithms are likely to bring added benefit. Interpret differences in various metric for their realistic impact Ask critical questions about evaluation strategy and hyperparameter choices Recommended reading This is the link to Keylinks, our online reference list. HDT4003 Period 2 28 Oct 2024 20 Dec 2024 Print course description ECTS credits: 3.0 https://maastrichtuniversity.keylinks.org/new-ui/hierarchy/list/2329 …