Speakers

ML and DL basics, supervised, reinforcement and unsupervised learning
This lecture introduces the fundamental concepts of Machine Learning (ML) and Deep Learning (DL) as key approaches in modern data-driven systems. The session covers the basic principles of learning from data, the differences between classical machine learning and deep learning models, and the role of labeled and unlabeled data in model training. Special attention is given to the main learning paradigms: supervised, unsupervised, and reinforcement learning. Supervised learning is presented through typical tasks such as classification and regression, unsupervised learning through clustering and representation learning, and reinforcement learning through agent–environment interaction and reward optimization. The lecture also discusses typical applications, strengths, and limitations of each approach, providing an intuitive understanding of when and why particular learning paradigms are used. By the end of the lecture, students will have a conceptual foundation for further study of machine learning algorithms and deep neural networks.

Python for data analysis, data visualization, and data mining

Exploring core Python libraries and methods for analyzing, visualizing, and mining data. Data preparation, exploratory data analysis, application of statistical and machine learning methods.