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

Allgemeine Informationen

Veranstaltungsname	Vorlesung: Search Engines and Neural Information Retrieval (Lecture)
Veranstaltungsnummer	INF-0506
Semester	SS 2024
Aktuelle Anzahl der Teilnehmenden	22
Heimat-Einrichtung	Professur für Sprachverstehen mit Anwendung Digital Humanities
Veranstaltungstyp	Vorlesung in der Kategorie Lehre
Erster Termin	Freitag, 19.04.2024 08:15 - 09:45, Ort: (202 F1 (Alte Uni))
Art/Form	Interactive teaching (on-site) with self-study components
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Ja
Hauptunterrichtssprache	englisch
ECTS-Punkte	8

Lehrende

• Prof.Dr. Annemarie Friedrich

Räume und Zeiten

(202 F1 (Alte Uni))

Freitag: 08:20 - 09:50, wöchentlich (12x)

Freitag: 10:00 - 11:30, wöchentlich (12x)

Freitag: 12:15 - 13:45, wöchentlich (12x)

(F2 503 (Alte Uni))

Freitag: 08:20 - 09:50, wöchentlich (1x)

Freitag: 10:00 - 11:30, wöchentlich (1x)

Freitag: 12:15 - 13:45, wöchentlich (1x)

(1033N (Campus))

Freitag: 08:20 - 09:50, wöchentlich (1x)

Freitag: 10:00 - 11:30, wöchentlich (1x)

Freitag: 12:15 - 13:45, wöchentlich (1x)

Studienbereiche

• Fakultät für Angewandte Informatik > Informatik

Modulzuordnungen

• Wirtschaftsinformatik Masterstudiengang Wirtschaftsinformatik (PO 2017), 15. Version (SS 2024) > Modulgruppe C: Minor Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 6. Version (SS 2024) > Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Informatik und Multimedia M.Sc. Informatik und Multimedia, PO 2018, 12. Version (SS 2024) > Wahlbereich Informatik und Multimedia > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Informatik M.Sc. Informatik, PO 2018, 12. Version (SS 2024) > Wahlbereich Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Wirtschaftsinformatik Masterstudiengang Wirtschaftsinformatik (PO 2017), 15. Version (SS 2024) > Modulgruppe D: Major Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Informatik und Multimedia M.Sc. Informatik und Multimedia, PO 2018, 12. Version (SS 2024) > Praktische Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Informatik M.Sc. Informatik, PO 2018, 12. Version (SS 2024) > Praktische Informatik > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Austauschstudium - FAI Austauschstudium FAI, 4. Version gültig ab SS 2024 > Fakultät für Angewandte Informatik - Master Level > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)
• Medizinische Informatik M.Sc. Medizinische Informatik, PO 2021, 6. Version (SS 2024) > Wahlbereich > INF-0506 - Search Engines and Neural Information Retrieval > Search Engines and Neural Information Retrieval (Lecture)

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.