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Бакалавриат 2024/2025

Научно-исследовательский семинар "Машинное обучение и приложения 1"

Статус: Курс по выбору (Компьютерные науки и анализ данных)
Направление: 01.03.02. Прикладная математика и информатика
Когда читается: 3-й курс, 1-4 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 3

Course Syllabus

Abstract

The research seminar is aimed at developing the skills of students to participate in research activities, getting acquainted with modern methods of machine learning and their practical application, developing skills in presenting research results and designing presentation materials.
Learning Objectives

Learning Objectives

  • The seminar is designed to facilitate the timely inclusion of students in the research process,
  • the development of skills for conducting scientific discussion and presentation of research results.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students are well-versed in what is the modern scientific direction of optimization methods
  • Students know both proven approaches in classical machine learning and modern research
  • Students have a basic idea about the main directions in DL
Course Contents

Course Contents

  • Optimization methods
  • Classical Machine learning
  • Deep learning
Assessment Elements

Assessment Elements

  • Partially blocks (final) grade/grade calculation Grade_report
  • blocking Grade_tests
  • Partially blocks (final) grade/grade calculation Grade_references
Interim Assessment

Interim Assessment

  • 2024/2025 1st module
    Правила округления итоговой оценки: Grade_result = 0.4 * Grade_tests + 0.4 * Grade_report + 0.2 * Grade_references Grade_tests - the total score for the tests at the seminars Grade_report - a summary assessment of two scientific presentations Grade_references - the total score for writing reviews on reports All intermediate grades are not rounded. The final score is rounded mathematically.
  • 2024/2025 4th module
    Правила округления итоговой оценки: Grade_result = 0.4 * Grade_tests + 0.4 * Grade_report + 0.2 * Grade_references Grade_tests - the total score for the tests at the seminars Grade_report - a summary assessment of two scientific presentations Grade_references - the total score for writing reviews on reports All intermediate grades are not rounded. The final score is rounded mathematically.
Bibliography

Bibliography

Recommended Core Bibliography

  • Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.

Recommended Additional Bibliography

  • Introduction to machine learning, Alpaydin, E., 2020

Authors

  • Ахмедова Гюнай Интигам кызы
  • BREDIKHIN ALEKSANDR IVANOVICH
  • PROMYSLOV VALENTIN VALEREVICH