Vorlesung: Search Engines and Neural Information Retrieval (Lecture) - Details

Vorlesung: Search Engines and Neural Information Retrieval (Lecture) - Details

Allgemeine Informationen

Veranstaltungsname Vorlesung: Search Engines and Neural Information Retrieval (Lecture)
Veranstaltungsnummer INF-0506
Semester WS 2026/27
Aktuelle Anzahl der Teilnehmenden 55
Heimateinrichtung Lehrstuhl für Computerlinguistik
Veranstaltungstyp Vorlesung in der Kategorie Lehre
Nächster Termin Mittwoch, 04.11.26, 14:00 - 15:30 Uhr N1002
Art/Form Interactive teaching (on-site) with self-study components
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Ja
Hauptunterrichtssprache englisch
ECTS-Punkte 8

Räume und Zeiten

N1002, tba

  • Mittwoch, 14:00 - 15:30, Wöchentlich (ab dem 21.10.26), Tutorial / Office Hour

N1005

  • Freitag, 08:15 - 09:45, Wöchentlich (ab dem 16.10.26)

1005 N

  • Freitag, 10:00 - 11:30, Wöchentlich (ab dem 16.10.26)
  • Freitag, 12:15 - 13:45, Wöchentlich (ab dem 16.10.26), Interactive Tutorial

Modulzuordnungen

Kommentar/Beschreibung

Neural Information Retrieval leverages the power of neural networks to enhance the representation, understanding, and retrieval of information, addressing many of the challenges posed by the complexity and variability of natural language. With the recent development in the area of large language models (or more generally, foundation models), novel approaches to interactive information retrieval are developing.

After taking part in the event, students are able to explain the concepts and methods, procedures, techniques and technologies related to neural information retrieval. In particular, the course covers:
• Basics of traditional information retrieval methods
• Vector-based document and query representations (topic modeling and neural representations)
• Ranking with embeddings
• Question answering, entity search, and knowledge graphs
• Multimodal retrieval
• Interactive information retrieval and personalization

This on-site course will be taught in an interactive way.
NOTE: The exercise session currently overlaps with the lecture Probabilistic Machine Learning lecture. In case you are planning to take both courses, please contact me. Another potential time slot for the tutorial session would be Wednesdays 8.15am (on campus). In this interactive course, there is no strict separation between lecture and tutorial, hence, it is important to participate in all sessions.