In spite of the advent of Machine Learning (ML) and its successful deployment in measurement systems, little information can be found in the literature about uncertainty quantification in these systems [1]. Uncertainty is crucial for the adoption of ML in commercial products and services. Designers are now being encouraged to be upfront about the uncertainty in their ML systems, because products that specify their uncertainty can have a significant competitive advantage and can unlock new value, reduce risk, and improve usability [2]. In this article, we will describe uncertainty quantification in ML. Because there isn't enough room in one article to explain all ML methods, we concentrate on Deep Learning (DL), which is one of the most popular and effective ML methods in I&M [3]. Please note that this article follows and uses concepts from Part 1 [4], so readers are highly encouraged to first read that part. In addition, we assume the reader has a basic understanding of both DL and uncertainty. Readers for whom this assumption is false are encouraged to first read the brief introduction to DL and its applications in I&M presented in [3] as well as the uncertainty tutorial in [5].

中文翻译：

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测量中的机器学习第2部分：不确定性量化

尽管机器学习（ML）的出现及其在测量系统中的成功部署，在文献中几乎找不到有关这些系统中不确定性量化的信息[1]。不确定性对于在商业产品和服务中采用机器学习至关重要。现在，鼓励设计人员提前了解其ML系统中的不确定性，因为指定不确定性的产品可以具有显着的竞争优势，并且可以释放新价值，降低风险并提高可用性[2]。在本文中，我们将描述ML中的不确定性量化。因为一篇文章中没有足够的空间来解释所有ML方法，所以我们将重点放在深度学习（DL）上，它是I＆M中最流行，最有效的ML方法之一[3]。请注意，本文遵循并使用了第1部分[4]中的概念，因此强烈建议读者首先阅读该部分。此外，我们假设读者对DL和不确定性都有基本的了解。鼓励对此假设不正确的读者首先阅读[3]中介绍的DL及其在I＆M中的应用的简要介绍，以及[5]中的不确定性教程。