Bachelor 2024/2025

Research Seminar "Data analysis in complex systems"

Type: Elective course (Data Science and Business Analytics)

Delivered by: School of Data Analysis and Artificial Intelligence

When: 4 year, 1-3 module

Open to: students of one campus

Instructors: Vasilii Gromov

Language: English

Course Syllabus

Abstract

The course covers modern machine learning and data analysis techniques, with an emphasis on clustering, time series forecasting, neural networks and information-theoretical approaches. The first part discusses density-based clustering algorithms, methods for extracting informative features and co-clustering, as well as working with high-dimensional data including the use of decision trees. In the second part, we focus on forecasting: analyzing the randomness of time series, estimating algorithms and multi-step forecasting based on clustering. Third, we explore modern methods of data representation, advanced neural network architectures such as constructive networks and neurodifferential equations, and integration of information theory. Finally, we conclude with the topic of adaptive learning using entropy metrics and introducing domain ontologies into model structures.