The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML.

自从基本的统计方法（例如线性回归到复杂的深度学习模型）以来，机器学习（ML）在文化遗产（CH）中的应用已经发展起来。问题仍然在于，与在“黑匣子”设置中使用该算法相比，该算法在基础算法上有多少积极改进。我们对ML和CH文献进行了调查，以找出有助于算法的理论变化，进而使其适合CH应用。另外，最常见的是，当没有更改时，我们将检查CH应用程序，功能以及预处理/后处理，这些方法使该算法适合其使用。我们分析了机器学习，监督，半监督和无监督内的主要分歧，并反思了广泛使用的各种算法。通过这样的分析，