2025/2026
Анализ неструктурированных данных
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Статус:
Маго-лего
Когда читается:
1, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
40
Course Syllabus
Abstract
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.
Learning Objectives
- 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)
Expected Learning Outcomes
- 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
Course Contents
- Text preprocessing. Pandas for text. Regex. TF-IDF.
- Syntax parsing. Spacy. Knowledge Graphs.
- Embeddings. Clustering. PCA. UMAP
- Language Models: n-grams. Marcov chains. Smoothing. Perplexity.
- Neural networks for text (FNN, Backprop). Text classification.
- RNN. LSTM.
- Transformer Architecture. BERT
- BERT. Large Language Models.
- Summarization and Domain adaptation
- Vector Databases. MultiAgents. RAG.
- Additional topics
Assessment Elements
- Домашнее задание 1. Загрузка, очистка и предобработка данных
- Домашнее задание 2. Классификация и кластеризация
- Проект.
- Презентация проекта
- Доклад
- Финальный тест
Interim Assessment
- 2025/2026 2nd module0.2 * Домашнее задание 2. Классификация и кластеризация + 0.1 * Доклад + 0.2 * Финальный тест + 0.2 * Домашнее задание 1. Загрузка, очистка и предобработка данных + 0.2 * Проект. + 0.1 * Презентация проекта
Bibliography
Recommended Core Bibliography
- Data clustering : theory, algorithms, and applications, Gan, G., 2007
- Deep learning, Goodfellow, I., 2016
- Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
- Shay Cohen. (2019). Bayesian Analysis in Natural Language Processing : Second Edition. San Rafael: Morgan & Claypool Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102157
- Speech and language processing, Jurafsky, D., 2014
Recommended Additional Bibliography
- Manning, C. D., & Schèutze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=24399
- Statistical machine translation, Koehn, P., 2013