HIO; Representation Learning for Natural Language Processing (RL4NLP)

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.