Machine learning (ML) is the most growing field of computer science, driven by the ongoing explosion in the availability of data [Jordan, M. I. & Mitchell, T. M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, (6245), 255-260]. ML evolved from artificial intelligence (AI) and deals with many different problems and aspects to solve various tasks, including knowledge discovery, data mining, decision support etc.; a grand challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in complex data, which are often hidden and not accessible to a human expert. The classical focus is on two interrelated questions: “How can we build algorithms that automatically improve through experience?” and “What are the fundamental statistical, computational, and information-theoretic laws that govern all learning systems, methods and tools, including computers, humans, and organizations?” The study of ML is most important for the application in health informatics, due to the fact that the health sciences are turning increasingly into a data science, and ML can help to realize evidence-based decision-making and support the grand goals towards personalized medicine [Holzinger, A. 2014. Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning. IEEE Intelligent Informatics Bulletin, 15, (1), 6-14].
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