Natural Language Processing DSBA 2026/2027
Course Syllabus
DSBA Natural Language Processing 2026.
- DSBA (ПАД ФКН): Syllabus
Teachers and Assistants
| Role | Group 1 | Group 2 | |
|---|---|---|---|
| Lecturer | Gleb Kuzmin | ||
| Seminarists | Fedor Pakhurov | Daria Andreeva | |
Useful links
- GitHub: for posting weekly material, seminars, useful literature, etc.
- Telegram Сhat (the link is in Smart LMS): for the course announcements (HW, timetable changes, etc.).
Course Description
The course “Natural Language Processing” introduces fundamental and modern methods for processing and analyzing natural language at the intersection of machine learning, deep learning, and computational linguistics. The course covers classical NLP methods, neural approaches to text processing, language modeling, attention and Transformer-based architectures, and modern large language models (LLMs). Particular attention is given to practical applications of LLMs, including fine-tuning, retrieval-augmented generation, efficient inference, evaluation, tool use and agents, as well as multimodal models and selected topics in reliable and responsible NLP.
Grading System
Relevant grading formulas are presented on the official course home page (DSBA Natural Language Processing).
0.4 * Exam + 0.2 * Сolloq + 0.4 * Project
Project Assignment
TBA
Midterm Colloq
TBA
Exam
TBA