Today, machine learning methods are largely responsible for the success of many applications in industry, business and science. A key characteristic of such methods is learning from data. In contrast to hard-coded decision rules, learning is based on empirical values or historical data.
Machine learning methods are becoming increasingly important in application, as the global process of digitalization means that more and more data and experience is available from all areas, from production processes, the Internet of Things and healthcare to everyday life. Computers will be able to solve problems that in the past relied on human expertise. Advances in algorithm development and the performance of modern hardware will make this possible in the future.