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Книга
Integral Robot Technologies and Speech Behavior

Kharlamov A. A., Pantiukhin D., Borisov V. et al.

Newcastle upon Tyne: Cambridge Scholars Publishing, 2024.

Статья
Clique detection with a given reliability

Semenov D., Koldanov A. P., Koldanov P. et al.

Annals of Mathematics and Artificial Intelligence. 2024.

Глава в книге
Neural Networks for Speech Synthesis of Voice Assistants and Singing Machines

Pantiukhin D.

In bk.: Integral Robot Technologies and Speech Behavior. Newcastle upon Tyne: Cambridge Scholars Publishing, 2024. Ch. 9. P. 281-296.

Препринт
DAREL: Data Reduction with Losses for Training Acceleration of Real and Hypercomplex Neural Networks

Demidovskij A., Трутнев А. И., Тугарев А. М. et al.

NeurIPS 2023 Workshop. ZmuLcqwzkl. OpenReview, 2023

Time Series Analysis with Python

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты

Преподаватель

Развенская Ольга Олеговна

Развенская Ольга Олеговна

Course Syllabus

Abstract

The study of this discipline is based on the following courses: • Block of mathematical disciplines; • Block of programming discipline. To master the discipline, students must possess the following knowledge and competencies: • Programming skills; • Calculus, Linear algebra, Probability and Statistics. The main provisions of the discipline can be used in their professional activities. As a result of mastering the discipline the student will be able to (results): - use Python for data forecasting with different regression models; - use Python for classification and clustering with different algorithms; - use Python for recommendation systems algorithms. https://www.coursera.org/learn/competitive-data-science
Learning Objectives

Learning Objectives

  • The purpose of the discipline is to get acquainted with programming tool Python for modern methods of data analysis and machine learning
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to choose the method of data processing and perform the data processing by the selected method
  • Be able to solve the problems of data analysis competitions
  • Be able to use Python for recommendation systems algorithms
Course Contents

Course Contents

  • Module 1 - Introduction to Machine Learning. Regression
  • Module 2 - Classification and clustering
  • Module 3 - Recommender Systems
Assessment Elements

Assessment Elements

  • non-blocking Итоговый тест
    Итоговый тест на знание основных терминов и понятий из курса
  • non-blocking Отчет
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.5 * Отчет + 0.5 * Итоговый тест
Bibliography

Bibliography

Recommended Core Bibliography

  • Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

Recommended Additional Bibliography

  • Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293