Machine learning (ML) is used in industry, medicine, economy etc. for data analysis and knowledge discovery in databases, data mining, generation of knowledge bases for expert systems, for recognition and prediction learning, games playing, for natural language recognition and translation, web mining, for controlling dynamic systems, for speech, writing and image recognition etc. The basic principle of ML is describing (modeling) of events from data. The result can be a set of rules, functions, relations, equations, or probability distributions. The learned models try to explain the data and can be used for future decisions during the observation of the modeled process. The goal of the course is to present the theoretical basics and the basic principles of ML methods, the basic ML algorithms and their practical usage for knowledge discovery from data and for learning classification and regression models. Students will apply the theoretical knowledge on real-world problems from scientific and business environments.
***If foreign students are enrolled in the course it is held in English.
TeacherProf. Igor Kononenko, PhD