Vorlesung: Search Engines and Neural Information Retrieval - Details

Vorlesung: Search Engines and Neural Information Retrieval - Details

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Allgemeine Informationen

Veranstaltungsname Vorlesung: Search Engines and Neural Information Retrieval
Veranstaltungsnummer INF-0506
Semester WS 2025/26
Aktuelle Anzahl der Teilnehmenden 33
Heimat-Einrichtung Professur für Sprachverstehen mit Anwendung Digital Humanities
Veranstaltungstyp Vorlesung in der Kategorie Lehre
Nächster Termin Freitag, 19.12.2025 10:00 - 13:45, Ort: (1001 N)
Voraussetzungen Recommended: linear algebra, basic probability theory, Python programming.
While we recommend refreshing your knowledge of machine learning, prior experience in this area is not required. To support you, we will provide introductory videos covering the basics of machine learning, including topics such as logistic regression, gradient descent, and neural networks. These materials can be used both for refreshing existing knowledge and for catching up if you have no prior background in machine learning.
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Ja
Hauptunterrichtssprache englisch

Räume und Zeiten

(1001 N)
Freitag: 10:00 - 13:45, wöchentlich (15x)
(N2045)
Donnerstag, 13.11.2025 18:00 - 19:30
(N 2045)
Mittwoch, 04.03.2026 10:00 - 12:00

Modulzuordnungen

Kommentar/Beschreibung

Neural Information Retrieval (Neural IR) applies neural network models to improve how information is represented and retrieved, addressing the complexities of natural language. With the rise of large language models and foundation models, new approaches are emerging for interactive, personalized, and multimodal information access. This course equips students with the concepts, techniques, and technologies behind traditional and neural IR, including embeddings, ranking, question answering, and knowledge graphs.

Prerequisites:
Recommended: linear algebra, basic probability theory, Python programming.
While we recommend refreshing your knowledge of machine learning, prior experience in this area is not required. To support you, we will provide introductory videos covering the basics of machine learning, including topics such as logistic regression, gradient descent, and neural networks. These materials can be used both for refreshing existing knowledge and for catching up if you have no prior background in machine learning.