Бакалавриат 2025/2026

Интеллектуальный анализ данных

Статус: Курс обязательный (Управление цифровым продуктом)

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 3-й курс, 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Саночкин Юрий Ильич

Язык: английский

Кредиты: 5

Контактные часы: 50

Course Syllabus

Abstract

The course «Intellectual data analysis» is designed to develop the competences of students in such actively developing and promising areas of knowledge, as analysis and visualization of data and combines the study of modern information technologies and Business Intelligence systems with traditional approaches to solving problems of management, economy, business analysis.

Learning Objectives

• The course is aimed to deepen students' understanding of advanced machine learning algorithms and techniques, including deep learning, ensemble methods, and unsupervised learning approaches.
• During the learning process, students will acquire practical skills in tuning, optimizing, and deploying machine learning models in production environments.
• Students will learn to critically evaluate model performance, interpret results, and address ethical and fairness considerations in machine learning applications.
• The course will enable students to independently conduct research and implement complex ML solutions for real-world problems, collaborating effectively in interdisciplinary teams.

Expected Learning Outcomes

• Be able to set up python environment for ML task;
• Understand key concepts of ML, current trends of AI;
• Be able to pass through all steps of DS task: EDA, process missing data and outliers, train an ML model, evaluate an ML model;
• Be able to find and read articles about ML applications.

Course Contents

• 1. Introduction to machine learning. Types of ML tasks and model classes.
• 5. Trees. Ensemble of tries
• 6. Introduction into Deep Learning
• 7. NLP and DL
• 8. Unsupervised ML algorithms
• 9. Recommender Systems

Assessment Elements

• HA
  The average grade for all practical homework assignments provided in the course
• EX
  The exam is a practical assignment completed by students upon completion of the course.
• GP
  The average grade for all practical group initiative projects provided in the course.
• ACT
  Assessment of student participation during seminars, as well as participation during lectures.
• ATT
  Assessment of student attendance during seminars, as well as during lectures.

Interim Assessment

• 2025/2026 4th module
  0.2 * ACT + 0.1 * ATT + 0.3 * EX + 0.25 * GP + 0.15 * HA

Bibliography

Recommended Core Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
• Pattern recognition and machine learning, Bishop, C. M., 2006

Recommended Additional Bibliography

• Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
• Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.

Authors

• Sanochkin Yuriy Ilich