Магистратура
2022/2023
Программирование на языке Python
Статус:
Курс обязательный (Прикладная лингвистика и текстовая аналитика / Applied Linguistics and Text Analytics)
Направление:
45.04.03. Фундаментальная и прикладная лингвистика
Где читается:
Факультет гуманитарных наук (Нижний Новгород)
Когда читается:
1-й курс, 2 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
66
Охват аудитории:
для своего кампуса
Преподаватели:
Каратецкая Ефросиния Юрьевна
Прогр. обучения:
Прикладная лингвистика и текстовая аналитика
Язык:
английский
Кредиты:
3
Контактные часы:
28
Course Syllabus
Abstract
The course consists of sections devoted to the study of text data processing methods. As part of the training, it is supposed to master the methods of preprocessing text data, algorithms for solving classical problems based on classical machine learning and deep neural networks.
Learning Objectives
- Study of methods and approaches for automatic processing of text data using the theory of classical machine learning and deep neural networks.
Expected Learning Outcomes
- A student understands the basics of word-to-vector representations, is familiar with the main approaches of text classification, writes examples of programs in Python
- A student is familiar with basic methods of preprocessing and feature extraction applied to the TA
- A student understands the basics of sequence-to-sequencerepresentations, is familiar with such terms as Sequence Markup. Seq2seq, MT, attention, transformer, writes examples of programs in Python
- A student is familiar with the some pre-trained language models, writes an examples of programs in Python
- A student understands the basics of thematic modeling, is familiar with the main approaches of text summarization and simplification, writes the examples of programs in Python
- A student is familiar with the main automatic methods for analyzing the sentiment of documents in general and by aspects, writes the examples of programs in Python
Interim Assessment
- 2022/2023 2nd module0.1 * Групповой доклад на семинаре + 0.9 * Домашняя контрольная работа
Bibliography
Recommended Core Bibliography
- Прикладной анализ текстовых данных на Python : машинное обучение и создание приложений обработки естественного языка, Бенгфорт, Б., 2020
Recommended Additional Bibliography
- Learning Python : [covers Python 2.5], Lutz, M., 2008
- Python 3, Прохоренок, Н. А., 2016
- Python и анализ данных, Маккинли, У., 2015
- Python. Самое необходимое, Прохоренок, Н. А., 2015