Principled Machine Learning

We introduce the underlying concepts which give rise to some of the commonly usedmachine 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 nonparametric 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.

Automated summary

This paper introduces the fundamental concepts of some commonly used machine learning methods, excluding deep-learning machines and neural networks. It discusses their advantages, limitations, and potential applications in various fields of photonics. The main methods covered include parametric and nonparametric regression and classification techniques, kernel-based methods, support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods, and visual informatics.