Representation Learning for Natural Language Processing (RL4NLP)
Kursusindhold
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.
Representation Learning for Natural Language Processing (RL4NLP)
IT & Cognition
This course will give the students:
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.
Our teaching methodology will combine traditional lectures with
interactive exercise classes and hands-on project work. We provide
individual supervision for projects and assignments and offer
students the opportunity to engage with current scientific
literature by presenting papers during the course. As we evolve, we
aim to transition towards a flipped classroom model to enhance the
student's learning journey further.
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.
- ECTS
- 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
- Kategori
- Timer
- Holdundervisning
- 28
- Forberedelse (anslået)
- 105
- Eksamensforberedelse
- 73
- Total
- 206
Kursusinformation
- Undervisningssprog
- Engelsk
- Kursusnummer
- HIOK0010EU
- ECTS
- 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
Kursusansvarlig
- Ali Basirat (4-65706d66446c7971326f7932686f)
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Kursusinformation for indskrevne studerende