Natural Language Processing DSBA 2026/2027: различия между версиями
Ekononova (обсуждение | вклад) Новая страница: «== Course Syllabus == DSBA Natural Language Processing 2026. * DSBA (ПАД ФКН): [https://www.hse.ru/ba/data/courses/1163570047.html Syllabus] == Teachers and Assistants == {| class="wikitable" style="text-align:center" |- ! Role !! Group 1 !! Group 2 |- || '''Lecturer''' | colspan="3" | [https://t.me/glkuzi Gleb Kuzmin] |- || '''Seminarists''' || [https://t.me/fpakhurov Fedor Pakhurov] || [https://t.me/xufana Daria Andreeva] |} == Useful links ==...» |
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# [https://github.com/xufana/dsba_nlp '''GitHub''']: for posting weekly material, seminars, useful literature, etc. | # [https://github.com/xufana/dsba_nlp '''GitHub''']: for posting weekly material, seminars, useful literature, etc. | ||
# Telegram Сhat | # Telegram Сhat: for the course announcements (HW, timetable changes, etc.). https://t.me/ +l7PWZn6sbctiY2Uy (erase the space in the link) | ||
# [https://drive.google.com/drive/folders/1QSdwPgUJYYY3NSi8lA-M-TgYeYPbAjb3?usp=sharing '''GoogleDrive''']: lecture materials, presentations. | |||
== Course Description == | == Course Description == | ||
Текущая версия от 08:14, 3 сентября 2026
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: for the course announcements (HW, timetable changes, etc.). https://t.me/ +l7PWZn6sbctiY2Uy (erase the space in the link)
- GoogleDrive: lecture materials, presentations.
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