Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

翻译：机器学习（ML）是指基于大量数据预测有意义输出或对复杂系统进行分类的计算机算法。机器学习广泛应用于自然科学、工程、太空探索乃至游戏开发等多个领域。本综述聚焦于机器学习在化学与生物海洋学领域的应用。在预测全球固定氮水平、二氧化碳分压及其他化学性质方面，机器学习的应用是一种极具前景的工具。机器学习还应用于生物海洋学领域，通过各类图像（如显微镜、FlowCAM及视频记录仪）、光谱仪及其他信号处理技术检测浮游生物形态。此外，机器学习成功利用声学特征对哺乳动物进行分类，并检测特定环境中濒危哺乳动物与鱼类物种。尤为重要的是，基于环境数据的机器学习被证实是预测低氧条件及有害藻华事件的有效方法，这对环境监测至关重要。更进一步，机器学习被用于构建多种物种数据库，为其他研究者提供实用资源，而新算法的开发将帮助海洋研究界更深入地理解海洋的化学与生物学特性。