Principled machine learning

N1 - UKRI Rights Retention: For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising Funding: DS acknowledges support from the EPSRC Programme Grant TRANSNET (EP/R035342/1) and the Leverhulme trust (RPG-2018-092). YR acknowledges support by the EPSRC Horizon Digital Economy Research grant'Trusted Data Driven Products: EP/T022493/1 and grant'From Human Data to Personal Experience': EP/M02315X/1. N2 - We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods and visual informatics.