Machine learning applications in science

Machine learning is a broad field of study, with multifaceted applications of cross-disciplinary breadth that ultimately aims at developing computer algorithms that improve automatically through experience. The core idea of artificial intelligence technology is that systems can learn from data, so as to identify distinctive patterns and make consequently decisions, with minimal human intervention. The range of applications of these methodologies is already extremely vast, and still growing at a steady pace due to the pressing need to cope with the efficiently handling of big data. In parallel scientists have increasingly become interested in the potential of Machine Learning for fundamental research, for example in physics, biology and engineering. To some extent, this is not too surprising, since both Machine Learning algorithms and scientists share some of their methods as well as goals. The two fields are both concerned about the process of gathering and analyzing data to design models that can predict the behavior of complex systems. However, the fields prominently differ in the way their fundamental goals are realized. On the one hand, scientists use knowledge, intelligence and intuition to inform their models, on the other hand, Machine Learning models are agnostic and the machine provides the intelligence by extracting it from data often giving little to no insight on the knowledge gathered. Machine learning tools in science are therefore welcomed enthusiastically by some, while being eyed with suspicions by others, albeit producing surprisingly good results in some cases. In this thesis we will argue, using practical cases and applications from biology, network theory and quantum physics, that the communication between these two fields can be not only beneficial but also necessary for the progress of both fields.