Overview

The course aims to provide a systematic introduction to modern machine learning models, starting from basic concepts and mathematical foundations and delving into deep aspects such as attention mechanisms in neural networks and geometric machine learning. The course covers the application of machine learning to various types of data, including text, images, time series, and others.

The course contains both sufficient theoretical material and practical seminars using datasets of different natures. The coursework involves implementing machine learning algorithms to consolidate understanding of the theory, as well as lab work to acquire skills of conducting full-cycle workflow. Upon successful completion, participants will be able to apply basic machine learning techniques in practice, explain the obtained results, and comfortably explore advanced courses in various machine learning areas.

The course is designed for technical professionals who seek a deep understanding of the structure of modern machine learning techniques. It is suitable for both beginners and practising specialists who wish to systematise and expand their knowledge in this area.