Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

机器学习 (ML) 是一系列功能强大的计算算法，能够通过对相对较大且通常为非结构化的数据进行智能自主分析来生成预测模型。机器学习已经成为我们日常生活中不可或缺的一部分，有着大量的应用，包括网络、商业、汽车工业、临床诊断、科学研究，以及最近的法医学。在法医 DNA 领域，复杂数据的手动分析可能具有挑战性、耗时且容易出错。基于机器学习的新颖方法的集成可能有助于简化这一过程，同时保持取证工具所需的高精度和可重复性。由于此类应用程序相对新颖，取证社区很大程度上不了解机器学习的功能和限制。此外，计算机科学和机器学习专业人士通常不熟悉法医学领域及其具体要求。本手稿简要介绍了机器学习方法的功能及其在法医 DNA 分析中的应用，并对这一快速发展领域的当前文献进行了批判性回顾。