Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

The series Lecture Notes in Physics (LNP), founded in 1969, reports new developments in
physics research and teaching-quickly and informally, but with a high quality and the explicit …

Deep learning has transformed the use of machine learning technologies for the analysis of
large experimental datasets. In science, such datasets are typically generated by large-scale …

Machine learning is a field of computer science that seeks to build computers capable of
discovering meaningful information and making predictions about data. It is the core of …

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

This paper reviews some of the challenges posed by the huge growth of experimental data
generated by the new generation of large-scale experiments at UK national facilities at the …