HIO; Representation Learning for Natural Language Processing (RL4NLP)

Kursusindhold

Representation Learning for Natural Language Processing (RL4NLP) focuses on machine learning techniques for embedding language units such as words, sentences, documents, and knowledge in a continuous feature space. The course elaborates on methods for training such features, applying them in downstream tasks, and interpreting them from a computational linguistic point of view.
 

The course educates students on the most effective techniques, ranging from static word embedding methods to more advanced contextual embeddings and approaches based on attention mechanisms and language modeling. Following this technical grounding,  students gain the theoretical insight and practical experience necessary to conduct research and develop a master’s thesis in this area.

 

The course is particularly relevant for students in the IT & Cognition program, computer science, and students from other disciplines with a sufficient mathematical and computer science background.

Engelsk titel

Representation Learning for Natural Language Processing (RL4NLP)

Uddannelse

IT & Cognition

Målbeskrivelse

At the examination, the student can demonstrate:

Knowledge and understanding of:

  • Theories and methods relevant to representing linguistic elements such as words, sentences, documents, and structured knowledge
  • Problems related to embedding natural language elements into numerical feature spaces, including training and interpreting implicit patterns learned by deep learning models in NLP


Skills in:

  • Discuss and document problems of relevance to language embeddings and representation learning
  • Propose and evaluate solutions for relevant problems in representation learning
  • Implement the foundational units of representation learning


Competencies in:

  • Describing and analysing advanced topics within the field and applying them to real-world problems
  • Designing, developing, and clearly documenting effective solutions to problems within the domain.


MA-level 
2019 curriculum

See all the curriculums.

Lectures and class instructions, student presentations, and group/individual projects based on the number of students

- Linear Algebra (vector space, matrix operations, matrix decomposition)
- Natural Language Processing (corresponding to the course Language Processing)
- Deep learning (Pytorch, feed-forward neural and recurrent neural networks)
- Programming experience in Python (corresponding to the course scientific programming in IT & Cognition)

Løbende feedback i undervisningsforløbet
Feedback ved afsluttende eksamen (ud over karakteren)
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
HIOK0011EU
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   (4-6974716a48707d7536737d366c73)
Gemt den 13-05-2025

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students