Анализ неструктурированных данных

2024/2025

ID 712947

Лучший по критерию «Новизна полученных знаний»

Статус: Маго-лего

Кто читает: Международная лаборатория прикладного сетевого анализа

Когда читается: 1, 2 модуль

Охват аудитории: для своего кампуса

Язык: русский

Кредиты: 6

Дополнительные материалы в LMS Задать вопрос

Аннотация

This course focuses on applied methods and existing tools for information retrieval: web scrap-ing, data preprocessing, natural language processing. All methods considered in this course require basic knowledge of discrete mathematics and probabilistic theory. For instance, most NLP and IR methods use conditional probability. In this course, we show the implementation of contemporary approaches in existing software packages (preferably in the python frameworks), and demonstrate how these methods can be used for the solution of some real-world problems.

Цель освоения дисциплины

to show the implementation of contemporary approaches in existing software packages (preferably in the python frameworks), and demonstrate how these methods can be used for the solution of some real-world problems.
to introduce the classical methods of natural language processing and data preprocessing (text cleaning and tokenization, vectorization, TF-IDF weighting, statistical language models and classical machine-learning classifiers), and to show how they are implemented in standard Python libraries (NLTK, scikit-learn, spaCy) and applied to real text-processing problems;
to explain the deep-learning methods and algorithms underlying modern NLP (word embeddings, recurrent and convolutional networks, the attention mechanism and the Transformer architecture), and to demonstrate their implementation in Python deep-learning frameworks such as PyTorch and their use for the solution of practical tasks;
to acquaint students with contemporary large language models and the techniques of working with them (pretraining and fine-tuning, parameter-efficient fine-tuning such as LoRA, prompt engineering and retrieval-augmented generation)

Планируемые результаты обучения

be able to criticize constructively and determine existing issues with applied nlp tasks
be able to get necessary data for research and applied projects
be able to perform basic ETL operations with datasets and unstructured data
have an understanding of the basic principles of information retrieval
have the skill to meaningfully develop an appropriate data analysis pipeline
have the skill to work unstructured text data
know advantages of existing natural language processing packages
know the basic principles behind the the existing deep learning approaches