In the lecture course, students learn elements of supervised machine learning with special emphasis on regression methods – linear regression, nearest neighbours, symbolic regression, regression trees, model ensembles and other methods. The course discusses methods for estimation of model's predictive performance as well as feature selection and optimization of model's structure which involves optimization algorithms, including the genetic algorithm.