392232 Privacy in Pangenomics (S) (WiSe 2023/2024)

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The recent surge of machine learning (ML) has opened up various opportunities when analyzing big datasets. Beyond basic, non-ML supported techniques of big data analytics, such as identifying similar items in big datasets, or arranging how to distribute jobs on large compute clusters, for example, the ML supported techniques enable to extract knowledge from large datasets at utmost diversity and accuracy.

The seminar will start with a mini lecture. First, lectures will explain how to cluster datasets. Clustering is an 'unsupervised' machine learning technique by which to mine social network graphs, for example. Second, 'supervised' machine learning techniques (where 'deep learning' likely is the most prominent recent technique) and their use in analyzing big data will be discussed. The mini lecture will be followed by seminar presentations, to be presented in small groups of 4-5 students.

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39-Inf-BDS Biomedical Data Science for Modern Healthcare Technology Ausgewähltes Seminar oder Projekt Study requirement
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39-Inf-WP-CLS-x Computational Life Sciences (Schwerpunkt) Begleitende Veranstaltung Seminar o. Übung Student information
- Graded examination Student information
39-Inf-WP-DS-x Data Science (Schwerpunkt) Begleitende Veranstaltung Seminar o. Übung Student information
- Graded examination Student information
39-M-Inf-ABDA Advanced Big Data Analytics / Big Data Machine Learning Machine Learning and AI in Advanced Big Data Analytics Ungraded examination
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39-M-Inf-ABDA_a Advanced Big Data Analytics / Big Data Machine Learning Machine Learning and AI in Advanced Big Data Analytics Graded examination
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39-M-Inf-INT-app Applied Interaction Technology Applied Interaction Technology: Seminar Student information
- Graded examination Student information
39-M-Inf-INT-app-foc Applied Interaction Technology (focus) Applied Interaction Technology (focus): Seminar Student information
- Graded examination Student information

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Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
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Registered number: 23
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WS2023_392232@ekvv.uni-bielefeld.de
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Last update basic details/teaching staff:
Friday, June 23, 2023 
Last update times:
Tuesday, August 15, 2023 
Last update rooms:
Tuesday, August 15, 2023 
Type(s) / SWS (hours per week per semester)
seminar (S) / 2
Language
This lecture is taught in english
Department
Faculty of Technology
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427117679