Machine learning is the most growing subfield 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]. Machine learning evolved from artificial intelligence 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 the 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, including computers, humans, and organizations? The study of machine learning is most important for the application in health informatics. The health sciences are turning increasingly into a data intensive science, machine learning 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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ACM Digital Library > Computing Classification System (CSS)  > Computing methodologies > machine learning >artificial intelligence