NDAK19001U Advanced Topics in Natural Language Processing (ATNLP)
The purpose of this course is to expose students to selected advanced topics in natural language processing. The course will bring the students up to a level sufficient for writing their master thesis in this area. The course is relevant for computer science students, as well as students from other studies with a good mathematical background, and students in the IT & Cognition programme. Please refer to the recommended academic qualifications.
Examples of topics include:
Natural language understanding
Learning from multiple modalities
Deep generative models
Generative adversarial learning
* The exact list of topics in the current year will depend on the lecturers and trends in natural language processing research and will be announced on the course Absalon website. Feel free to contact the course organiser for details.
Selected advanced topics in natural language processing, including:
design of learning algorithms
evaluation of learning algorithms
Read and understand recent scientific literature in the field of natural language processing
Apply the knowledge obtained by reading scientific papers
Compare methods and assess their potentials and shortcomings
Understand advanced methods, and to transfer the gained knowledge to solutions to practical problems
Plan and carry out self-learning
It is assumed that the students have successfully passed either the “Natural Language Processing” course from KU, the “NLP and Deep Learning” course from ITU, or the “Language Processing 1” and “Language Processing 2” courses from KU. In case you have not passed one of these courses, please contact the course organiser to verify the suitability of your background prior to signing up for the course.
Academic qualifications equivalent to a BSc degree is recommended.
- Practical exercises
- Project work
- 7,5 ECTS
- Type of assessment
- Continuous assessmentThe assessment is based on the following five parts:
1. Completion of weekly quizzes.
2. Class presentation of an academic paper.
3. Peer feedback on the presentation(s) of the class presentations of other students.
4. Group presentation of the group project to re-implement a model from the literature.
5. An individual 5-page written report on student's efforts to replicate the model and the results of their replication.
Each part-exam is assessed and weighted individually, and the final grade is determined based on this.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
Several internal examiners.
The re-exam consists of two parts:
1. An individual written report. The written report is to be submitted no later than 3 weeks before the re-exam week.
2. A 30 minute oral examination without preparation.
The two parts will be given an overall assessment
Criteria for exam assesment
See learning outcome