Practical training: Praktikum Natual Language Processing - Details

General information

Course name	Practical training: Praktikum Natual Language Processing
Course number	INF-0441
Semester	SS 2023
Current number of participants	12
expected number of participants	15
Home institute	Embedded Intelligence for Health Care and Wellbeing
participating institutes	Institut für Informatik
Courses type	Practical training in category Teaching
First date	Monday, 25.09.2023 09:00 - 19:00, Room: (1005 N)
Participants	The Praktikum will take place as a block course at the end of the winter term (February/March 2021).
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Yes
Hauptunterrichtssprache	englisch
ECTS points	5

Lecturers

• Prof.Dr. Björn Schuller
• Manuel Milling
• Dr.-Ing. Shahin Amiriparian
• Maurice Gerczuk
• Adrià Mallol Ragolta
• Alexander Kathan
• Lukas Christ
• Andreas Triantafyllopoulos

Rooms and times

(1005 N)

Monday, 25.09.2023 - Friday, 29.09.2023 09:00 - 19:00

Fields of study

• Fakultät für Angewandte Informatik > Informatik

Module assignments

• Informatik und Multimedia M.Sc. Informatik und Multimedia, PO 2018, 10. Version (SS 2023) > Wahlbereich Informatik und Multimedia > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Wirtschaftsinformatik Masterstudiengang Wirtschaftsinformatik (PO 2017), 13. Version (SS 2023) > Modulgruppe C: Minor Informatik > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Austauschstudium - FAI Austauschstudium FAI, 2. Version (SS 2023) > Fakultät für Angewandte Informatik - Master Level > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Wirtschaftsinformatik Masterstudiengang Wirtschaftsinformatik (PO 2017), 13. Version (SS 2023) > Modulgruppe D: Major Informatik > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik M.Sc. Informatik, PO 2018, 10. Version (SS 2023) > Praktische Informatik > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik M.Sc. Informatik, PO 2018, 10. Version (SS 2023) > Wahlbereich Informatik > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik M.Sc. Informatik, PO 2011, 17. Version (SS 2023) > M.Sc. Informatik (PO '11), Teilbereich Informatik - Multimedia > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik und Multimedia M.Sc. Informatik und Multimedia, PO 2018, 10. Version (SS 2023) > Multimedia > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik und Multimedia M.Sc. Informatik und Multimedia, PO 2017, 13. Version valid from SS 2023 > M.Sc. Informatik und Multimedia (PO '17), Teilbereich Informatik - Multimedia > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing
• Informatik M.Sc. Informatik, PO 2017, 13. Version valid from SS 2023 > M.Sc. Informatik (PO '17), Teilbereich Informatik - Multimedia > INF-0441 - Praktikum Natural Language Processing > Praktikum Natural Language Processing

Comment/Description

It is highly recommended to complete the lecture "Deep Learning" (INF-0315) or similar courses prior to this course.
In the Natural Language Processing Praktikum, students will apply their machine learning knowledge to text data.
First, "traditional" methods to automatically process and analyse texts are introduced, giving the students some foundational intuitions on the nature of natural language data and the challenges in dealing with it. The main part of the course is devoted to recent Transformer-based Language Models in the fashion of BERT and its successors which have achieved remarkable success in virtually all areas of Natural Language Processing. The transformer architecture is investigated in depth. Afterwards, the students utilise pretrained Transformer Language Models to adress text classification and language generation tasks in a hands-on manner. Lastly, more advanced topics such as few-shot learning and textual style transfer are adressed. The focus of this course is programming, while the necessary background knowledge is provided by introductory tutorials.
NOTE: Fundamental knowledge of Deep Learning and first practical experience with either PyTorch or Tensorflow are required. PyTorch will be used as the deep learning framework in this course.
Please NOTE that you need to register via STUDIS in order to participate in the Praktikum.

Admission settings

The course is part of admission "Anmeldung gesperrt (global)".

The following rules apply for the admission:

• Admission locked.