LTAT.01.001 Natural Language Processing
The field of natural language processing (NLP) is in constant development. The most rapid changes began after the introduction of the Transformer architecture, which forms the basis of large language models (LLM). The goal of this course is to provide a modern, practical, and conceptually comprehensive introduction to natural language processing as it is practiced today.
Course info
This is an active learning course that uses the following main elements:
- Home readings covering theoretical material
- Workshops for discussing the readings in classroom
- Wednesdays at 16:15, Delta 1019
- Practicums for solving practical exercises
- Mondays at 16:15, Delta 2045
- Project for applying the acquired knowledge
- throughout the semester with intermediate milestones, presentations in the last week of the semester
Teaching staff
- Course Instructor: Kairit Sirts (kairit.sirts@ut.ee)
- TA: Navneet Agarwal (navneet.agarwal@ut.ee)
Course communication
TBA
Assessment
| Type | Points | Comment |
|---|---|---|
| Practicum exercises | 30 points | 12 practicums, 3 points each, best 10 count |
| Moodle tests based on readings | 10 points | 14 tests, 1 point each, best 10 count |
| In class workshops | 10+4 points | 14 workshops, 1 point each, this category can give extra points |
| Reading reflections | 10 points | 4 reading reflections, 3 points each, best 10 count |
| Project | 30 points | Done in groups of 2-3 people |
| Total | 104 points |
How to pass the course?
In order to pass the course, you must obtain at least 51 points from any course activities (reading tests and reflections, workshop participation, practicum exercises, and the project).
None of the course activities are compulsory. However, because this is an active learning course without traditional lectures, for the best learning experience it is strongly advised to prioritize attending the in-class workshops, where the core concepts will be discussed.
The maximum number of points in most assessment categories exceeds the number of points taken into account in the final grade. This is intended to provide a buffer for situations where, due to illness or other reasons, some tasks cannot be completed on time. Because this buffer is already built in to the assessment system, we do not accept late submissions for activities.
Course materials
All course materials and tasks are available in Moodle.
Workshops
This course has workshops instead of traditional lectures. In the workshops, we will actively discuss the concepts and ideas related to the topic of the week, based on the assigned readings. Workshops are in-class only, they will not be recorded and no hybrid participation is possible.
Practicums
Practicums are scheduled in the timetable, but they can be completed anywhere and at a time convenient for you. The classroom time is intended to provide a structured time and space for engaging in this self-study activity. The TA is present during the practicum time to provide help and guidance if needed. The TA will be in the classroom for at least 15 minutes and will leave if no one shows up within that time period.
Plagiarism
As expected, plagiarism is not allowed. Individual assignments must be completed strictly individually. In group assigments, every participant must make a contribution.
Using generative AI
Usage of generative AI is allowed in accordance to the university guidelines. Most importantly, you as a student are solely responsible for the content of your work.
Prerequisites
This course assumes knowledge from various areas. In Study Information System, the required prerequisite course is Machine Learning (MTAT.03.227), and the recommended prerequisite courses are Language Technology (LTAT.01.002) and Artificial Intelligence (LTAT.01.003). In practice, we also assume the basic knowledge of higher mathematics (calculus, linear algebra, probability theory) and computer programming (Python). If you lack some of the required knowledge, it is your responsibility to acquire it at the level necessary for progressing in this course.
Adaptations due to special needs
If you require adaptations due to special needs, please contact the course instructor as early as possible to discuss your needs and potential accommodations. Students are responsible for clearly communicating their needs, and, where appropriate, for obtaining guidance from the university counselling or support services to clarify which accommodations are necessary.
Please note that any accommodations must be compatible with the structure and learning objectives of the course. In particular, some course components (such as in-class workshops) are designed as interactive, in-person activities and may not be adaptable to alternative formats.