As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalize to small changes in the distribution, tend to be confident on novel data they have never seen, or cannot communicate the rationale behind their decisions effectively with the end users. Collectively, we face a trustworthiness issue with the current machine learning technology. This textbook on Trustworthy Machine Learning (TML) covers a theoretical and technical background of four key topics in TML: Out-of-Distribution Generalization, Explainability, Uncertainty Quantification, and Evaluation of Trustworthiness. We discuss important classical and contemporary research papers of the aforementioned fields and uncover and connect their underlying intuitions. The book evolved from the homonymous course at the University of T\"ubingen, first offered in the Winter Semester of 2022/23. It is meant to be a stand-alone product accompanied by code snippets and various pointers to further sources on topics of TML. The dedicated website of the book is https://trustworthyml.io/.

翻译：随着机器学习技术被应用于实际产品和解决方案，新的挑战随之涌现。模型会意外地在分布微小变化时无法泛化，倾向于对从未见过的新数据过度自信，或无法向最终用户有效传达其决策背后的逻辑。总体而言，当前机器学习技术面临信任问题。本《可信机器学习》（TML）教材涵盖了TML四个关键主题的理论与技术基础：分布外泛化、可解释性、不确定性量化及可信度评估。我们探讨了上述领域的重要经典与当代研究论文，揭示并连通其背后的直觉。本书源于蒂宾根大学同名课程（2022/23冬季学期首次开设），旨在成为独立学习资源，附有代码片段及指向TML主题更多资料的各类指引。本书专属网站为 https://trustworthyml.io/。