Магистратура
2026/2027
Анализ неструктурированных данных
ID 1136775
Статус:
Курс по выбору (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Где читается:
Факультет социальных наук
Когда читается:
2-й курс, 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.
- Vector Databases. MultiAgents. RAG.
- Additional topics
Assessment Elements
- Homework 1Students collect a text corpus by parsing a web source (for example, a news website), and then apply the classical NLP methods covered in the first part of the course: text cleaning and preprocessing, vectorization (Bag-of-Words / TF-IDF), and at least one text-classification and one clustering pipeline, with a short interpretation of the results.
- Homework 2Students fine-tune a pretrained language model on a chosen task and dataset using parameter-efficient fine-tuning (for example, LoRA), evaluate the resulting model on an appropriate metric, and briefly analyze the effect of the main training hyperparameters.
- Colloquium 1
- Colloquium 2
Interim Assessment
- 2026/2027 2nd module0.2 * Homework 2 + 0.3 * Colloquium 1 + 0.3 * Colloquium 2 + 0.2 * Homework 1
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