HIOK0010EU ITC; Specialization 3: Representation Learning for Natural Language Processing (RL4NLP)
ITC; Specialization 3: Representation Learning for Natural Language Processing (RL4NLP)
IT & Cognition
Representation Learning for Natural Language Processing (RL4NLP) is a course that focuses on studying techniques for representing language units such as words, sentences, documents, and knowledge in a numerical format. The course elaborates on methods for training such representations, interpreting them, and their application in downstream tasks.
At the examination, the student can demonstrate:
Knowledge and understanding of:
• theories and methods of relevance to representation learning for natural language elements such as words, sentences, documents, and knowledge when expressed through natural language
• problems related to embeddings of natural language elements into a numerical feature space and unveiling the implicit learning within deep learning methods for natural language processing.
Skills in
• discussing and documenting problems of relevance to language embeddings and representation learning
• proposing and evaluating solutions for relevant problems in
representation learning
Competencies in
• describing and analysing advanced topics within representation learning through deep neural networks for language
• designing and documenting relevant solutions.
MA-level
2019 curriculum
See all the curriculums.
Lectures: Engaging lectures and interactive sessions will provide comprehensive coverage of fundamental concepts and advanced topics in representation learning. They will also encourage participation and facilitate deeper understanding through discussions and Q&A sessions.
Project Work: Hands-on projects will allow students to apply their learning to real-world scenarios and tacke possible challenges in their study. They will be supported by individual supervision to ensure successful project completion.
Scientific Paper Presentations: Students will present scientific papers relevant to the course material. This activity will enhance research skills, presentation abilities, and critical analysis of scientific literature.
Transition to Flipped Classroom: In future course iterations, we will gradually implement the flipped classroom teaching method. Pre-recorded lectures and online resources will be provided before in-class sessions, allowing for more interactive and applied learning during class time. This approach enables the students to take ownership of their learning, fosters deeper engagement, and encourages active participation.
- Kategori
- Timer
- Holdundervisning
- 28
- Forberedelse (anslået)
- 105
- Eksamensforberedelse
- 73
- I alt
- 206
- Point
- 7,5 ECTS
- Prøveform
- Skriftlig aflevering
- Prøveformsdetaljer
- Take-home assignment, optional subject
- Hjælpemidler
- Alle hjælpemidler tilladt
- Bedømmelsesform
- 7-trins skala
- Censurform
- Ingen ekstern censur
- Reeksamen
Conducted in the same manner as the original exam but can only be taken individually
Kursusinformation
- Sprog
- Engelsk
- Kursuskode
- HIOK0010EU
- Point
- 7,5 ECTS
- Niveau
- Master
- Varighed
- 1 semester
- Placering
- Efterår
- Skemagruppe
- Uden for skemastruktur
Studienævn
- Studienævnet for Nordiske Studier og Sprogvidenskab
Udbydende institut
- Institut for Nordiske Studier og Sprogvidenskab
Udbydende fakultet
- Det Humanistiske Fakultet
Kursusansvarlige
- Ali Basirat (4-636e6b64426a776f306d7730666d)