Seminar Computing at Scale im maschinellen Lernen
Titel auf Englisch: Seminar Computing at Scale in Machine Learning
Lernergebnisse
After successfully completing the module, students will be familiar with state-of-the-art problems and methodological approaches in the intersection of large-scale computing and machine learning; that is, computing at scale for machine learning and machine learning for large-scale computation. They will have the ability to familiarize themselves with current research in the area from original research literature, to participate in a technical discussion within the context of international science, and present scientific content in written and oral form.
Inhalte
Students will learn about specific state-of-the-art problems and methodological approaches for large-scale computation in machine learning. The topics range from parallel computation on individual machines, to implicit parallelism frameworks on compute clusters, and algorithms and data structures supporting efficient exact or approximate computation with massive data sets in and out of core.
Voraussetzungen
Verpflichtende Voraussetzungen
Keine
Empfohlene Voraussetzungen
Knowledge of the content of the module 14038 Computing at Scale in Machine Learning: Distributed Computing and Algorithmic Approaches
Prüfungsmodalitäten
- Seminar presentation, 30-45 min depending on the subject (40%)
- Report on the topic of the seminar presentation, 10 pages (40%)
- Active participation (20%)
Angeboten in
- Artificial IntelligenceM.Sc.
Wahlpflichtkomplex: Seminars or Laboratories
- InformatikM.Sc.
Wahlpflichtkomplex: Seminare oder Praktika
| Studiengang | Abschluss | Wahlpflichtkomplex |
|---|---|---|
| Artificial Intelligence | M.Sc. | Seminars or Laboratories |
| Informatik | M.Sc. | Seminare oder Praktika |
Literatur und Materialien
Depending on the projects, the relevant literature or material will be shared in the first session of the semester.
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.