Seminar: Seminar Natural Language Understanding (Master) - Details

Seminar: Seminar Natural Language Understanding (Master) - Details

Allgemeine Informationen

Veranstaltungsname Seminar: Seminar Natural Language Understanding (Master)
Veranstaltungsnummer INF-0468
Semester SS 2026
Aktuelle Anzahl der Teilnehmenden 8
erwartete Teilnehmendenzahl 12
Heimateinrichtung Lehrstuhl für Computerlinguistik
Veranstaltungstyp Seminar in der Kategorie Lehre
Erster Termin Mittwoch, 15.04.26, 17:30 - 18:30 Uhr N1054
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Ja
Hauptunterrichtssprache englisch
ECTS-Punkte 4

Räume und Zeiten

N1054

  • Mittwoch, 15.04.26, 17:30 - 18:30 Uhr

BCM, 10th Floor, Room 1024

  • Dienstag, 21.04.26, 16:00 - 17:30 Uhr
  • Dienstag, 28.04.26, 16:00 - 17:30 Uhr
  • Dienstag, 05.05.26, 16:00 - 17:30 Uhr
  • Dienstag, 12.05.26, 16:00 - 17:30 Uhr
  • Dienstag, 19.05.26, 16:00 - 17:30 Uhr
  • Dienstag, 02.06.26, 16:00 - 17:30 Uhr

BCM 1024

  • Montag, 13.07.26, 09:00 - 13:00 Uhr

Kommentar/Beschreibung

This seminar introduces students to Natural Language Understanding (NLU), the field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. The seminar explores how modern approaches such as machine learning, deep learning, generative AI, and large language models (LLMs) are transforming natural language processing.

Students will learn the fundamental concepts behind language representation, neural networks, and transformer-based models, and how these techniques are applied in real-world systems such as chatbots, virtual assistants, search engines, and text analysis tools. The seminar also highlights current research trends, practical challenges, and ethical considerations related to large-scale language models.
Through paper discussions, hands-on exercises, and student presentations, participants will gain both theoretical understanding and practical insight into how state-of-the-art NLU systems are built and evaluated.

Topics may include:

* Basics of Natural Language Processing and Understanding
* Machine learning and deep learning for text data
* Transformer architectures and large language models
* Generative AI for text generation and dialogue systems
* Evaluation methods and limitations of language models
* Ethical, societal, and environmental implications of generative AI