Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML‐based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science, and ML.

中文翻译：

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使用机器学习解码光学数据

光谱和成像技术在疾病诊断、生物学研究、信息技术、光学科学和材料科学等许多领域发挥着重要作用。在过去的十年中，机器学习 (ML) 已被证明在解码复杂数据、实现快速准确的光谱和图像分析方面具有广阔的前景。本综述旨在阐明用于光学数据分析的各种机器学习算法，重点关注它们在广泛领域的应用。这项工作的目标是概述基于机器学习的光学数据解码的有效性。该评论最后对这个连接光学、数据科学和机器学习的新兴学科中尚未解决的问题和机遇进行了展望。