Advanced Topics in Natural Language Processing (ATNLP)
Course content
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:
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Natural language understanding
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Representation learning
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Multitask learning
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Learning from multiple modalities
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Deep generative models
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Reinforcement learning
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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.
Knowledge of
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Selected advanced topics in natural language processing, including:
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design of learning algorithms
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evaluation of learning algorithms
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Skills to
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Read and understand recent scientific literature in the field of natural language processing
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Apply the knowledge obtained by reading scientific papers
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Compare methods and assess their potentials and shortcomings
Competences to
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Understand advanced methods, and to transfer the gained knowledge to solutions to practical problems
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Plan and carry out self-learning
Lectures and class instructions, student presentations, peer feedback, group projects, project presentations
See Absalon.
The course requires a strong mathematical background and an
understanding of natural language processing. It is suitable for
computer science master students, as well as students from
mathematics (statistics, actuarial math, math-economics, etc), and
IT & Cognition study programmes, provided the latter have a
strong mathematical background. If you are a student from another
study programme we strongly advise you to contact the course
organiser and verify the suitability of your background prior to
signing up for the course.
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.
- ECTS
- 7,5 ECTS
- Type of assessment
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Continuous assessment
- Type of assessment details
- The assessment is based on the following five exams:
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 of the five exams is assessed and weighted individually, and the final grade is determined based on this. - Aid
- All aids allowed
For programming tasks specifically, the use of GitHub Copilot or similar AI-based programming tools is permitted. The finite list of allowed AI-tools will be announced in Absalon.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
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The re-exam consists of two exams:
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 with no preparation time.
The two exams will be given an overall assessment
Criteria for exam assessment
See learning outcome
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 60
- Practical exercises
- 33
- Project work
- 85
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK19001U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
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1 block
- Placement
- Block 2
- Schedulegroup
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A2
- Capacity
- 54
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student. - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Desmond Elliott (2-696a45696e33707a336970)
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Kursusinformation for indskrevne studerende