Vorlesung
2 SemesterwochenstundenBTU-Vorlesungsverzeichnis ↗- Donnerstag08 Okt 2026 bis 28 Jan 202715:30 bis 17:00
← Zurück zur Übersicht
14038Fakultät 1 – MINT – Mathematik, Informatik, Physik, Elektro- und Informationstechnik
Computing-at-Scale im Maschinellen Lernen: Verteiltes Rechnen und Algorithmische Ansätze
Titel auf Englisch: Computing at Scale in Machine Learning: Distributed Computing and Algorithmic Approaches
Lernergebnisse
After successfully completing the module, students have an overview on how to solve large-scale computational problems in data science and machine learning. They know parallel approaches from multi-threaded computation on individual machines to implicit parallelism frameworks on compute clusters. They are familiar with algorithms and data structures supporting efficient exact or approximate (e.g. sketching) computation with massive data sets in and out of core. They are able to implement the algorithms. They can assess which methods can be used in a given situation.
Inhalte
- A review of memory-compute co-location and its impact on big data computations.
- Solving Machine Learning (ML) work loads using explicit parallelism, specifically multi-threaded computation on an individual machine.
- Introduction of implicit parallelism programming models as implemented for example in MapReduce, Spark and Ray and their application in ML.
- Probabilistic algorithms such as sketching algorithms (incl. CountMinSketch, Hy-perLogLog) or Bloom filters.
- Implementing ML methods using index data structures such as suffix or kd-trees.suffix or kd-trees.
Voraussetzungen
Verpflichtende Voraussetzungen
Keine
Empfohlene Voraussetzungen
- Good working knowledge of discrete probability, algorithms and data structures at the undergraduate level is absolutely necessary
- Introduction to machine learning at Master’s level
- Advanced knowledge of programming in Python and the Linux command line
Prüfungsmodalitäten
- 3 out of 4 successfully completed quizzes (45 minutes each) in the exercises
- Written examination, 120 min. OR
- Oral examination, 30-45 min. The nature of the examination will be announced in the first lecture.
Termine im WiSe 26/27
Übung
2 SemesterwochenstundenBTU-Vorlesungsverzeichnis ↗- Donnerstag08 Okt 2026 bis 28 Jan 202717:30 bis 19:00
Prüfung
BTU-Vorlesungsverzeichnis ↗- Donnerstag18 Feb 202708:00 bis 10:00
Angeboten in
- Artificial IntelligenceM.Sc.
Wahlpflichtkomplex: Learning and Reasoning
- InformatikM.Sc.
Wahlpflichtkomplex: Praktische Informatik
- Mathematical Data ScienceM.Sc.
Wahlpflichtkomplex: Data Science Applications
| Studiengang | Abschluss | Wahlpflichtkomplex |
|---|---|---|
| Artificial Intelligence | M.Sc. | Learning and Reasoning |
| Informatik | M.Sc. | Praktische Informatik |
| Mathematical Data Science | M.Sc. | Data Science Applications |
Literatur und Materialien
- Data Science Design Manual. S. Skiena. Springer (Exerpts)
- Parallel Programming for Multicore and Cluster Systems. T. Rauber and G. Rünger. Springer (Exerpts)
- Review and Original Research Articles
Bewertungen von Studierenden
Was Studierende über dieses Modul sagen. Die BTU hat damit nichts zu tun — hier schreiben nur Studierende, und jede Bewertung ist die Meinung einer einzelnen Person.