Machine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated.

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为海洋科学中的机器智能时代奠定基础

机器学习是人工智能的一个子领域，提供了可用于海洋科学的各种方法。它支持数据驱动的学习，可以自动进行从头开始的决策数据。与劳动密集型且需要大量时间的手动分析相比，它具有显着的优势。机器学习方法具有巨大的潜力，可以通过识别潜在模式和隐藏趋势来提高海洋研究的质量和范围，尤其是在使用其他方法难以处理的大型数据集中。新的传感器技术支持从海洋环境中收集大量数据。迅速发展的机器学习子领域称为深度学习，它应用了受大脑结构和功能启发的算法（人工神经网络），能够通过在短时间内处理大型数据集来解决非常复杂的问题，有时其性能优于人类专家。鉴于机器学习可以提供的机会，将其纳入海洋科学和海洋资源管理是不可避免的。这套主题文章的目的是提供尽可能多的案例研究选择，以证明海洋科学中机器学习的应用，用途和前景。我们还通过展望机器学习已完全融入其中的未来海洋科学来提供前瞻性信息。