Machine Learning is a branch of artificial intelligence concerned with the design of algorithms that improve their performance based on empirical data. This course gives an in-depth coverage of the theory and principles of machine learning and gives an overview of machine learning scientific and industrial applications. The course covers two main approaches to machine learning: supervised learning (classification and regression) and unsupervised learning (clustering and dimensionality reduction), including generative and discriminative concepts within both approaches. Theoretical fundamentals of machine learnings are described (hypothesis, model, model selection, errors, overfitting, generalization), and an overview of supervised and unsupervised procedures is given, with links toward theoretical elements and corresponding mathematical fundamentals from optimization theory, numerical mathematics, statistics, probability theory and linear algebra. Advantages and disadvantages of each model are considered, and examples of successful application in industry are given. A special subject unit is focused on model testing and error analysis. Lectures are accompanied by laboratory exercises in the Scikit-Learn environment, where the participants obtain knowledge on applying and testing models on simple data. Prerequisites for following the course includes basic .