Бакалавриат
2026/2027
Классическое и нейросетевое моделирование
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
4-й курс, 1-3 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
9
Контактные часы:
96
Course Syllabus
Abstract
Modern research data analysis increasingly requires going beyond purely statistical or purely deterministic approaches. Real-world problems ranging from processing the results of physical experiments to modeling epidemiological, biological, or engineering processes pose fundamental questions for analysts: how to work with systems where data is sparse but theory is abundant; how to extract hidden parameters from observations; how to combine the accuracy of classical models with the flexibility of neural networks. This course offers a systematic view to modeling by integration three fundamental approaches: complex systems theory, classical numerical methods, and modern physics-informed neural network architectures. Designed for fourth-year students with advanced programming and data analysis skills, the course focuses on developing a research engineering culture, the ability to select a descriptive language based on the nature of the object and the nature of the data.