Data mining (which is often referred to as the machine learning, pattern analysis and frequently defined bymany other names) is one of central disciplines of modern-day computational science. Data mining deals with the problem of extracting (or "learning") "knowledge", "models", "algorithms" or other types of useful information from raw data. In our age of terabyte-sized pocket drives and high-speed data acquisition the need to get necessary knowledge from data is ubiquitous. Natural sciences such as physics or biology have “addressed” computerized data analysis and regard stable patterns extracted from experimental datato make them a central research target. Engineering disciplines such as robotics and AI are based on the obtained knowledge on how to devise sophisticated automated behaviours, ranging from business decision support and movie recommendations to autonomous warfare.
The course aims to give an overview of basic data mining and machine learning techniques. We shall discuss both theoretical basis and practical implementation as well as application issues of data mining. Participants are expected to have no significant prior knowledge. The only requirement is to be competent in probability theory and calculus at a high-school level. The course will include programming, but no prior experience in programming is demanded.
Tutor:
Konstantin Tretyakov (Department of Computer Science, Tartu University, Estonia)