Fraunhofer Institut Autonome Intelligente Systeme, Germany
While most data collections are huge and contain highly structured information, machine learning research has long assumed that the pieces of information are independent and can be stored as a single entity in a single table of fixed size. The more advanced part of this lecture will concentrate on kernel methods for graphs as these are one of the most popular representations for structured data in mathematics, computer science, engineering disciplines, and other natural sciences. We will focus on algorithmic aspects but also present applications and empirical results related to computational drug design.
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