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.
Frequency | Weekday | Time | Format / Place | Period |
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Module | Course | Requirements | |
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31-M-ASM2 Advanced Statistical Methods II | Veranstaltungen aus dem Bereich Statistik und/oder in (einem) methodisch verbundenen Gebiet(en) (I.) | Graded examination
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Student information |
39-Inf-AB Algorithmen der Bioinformatik | Ausgewähltes Seminar zu Algorithmen der Bioinformatik | Study requirement
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Student information |
39-Inf-SAB_a Spezielle Algorithmen der Bioinformatik | Ausgewähltes Seminar zu Spezielle Algorithmen der Bioinformatik | Study requirement
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Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
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Studieren ab 50 | - | - | - | - | - | - |
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