Trustworthy ML introduces concepts of trustworthiness in machine learning, drawn from computer science, systems engineering, human-computer interaction, psychology, and philosophy. Trustworthiness topics include robustness, uncertainty, fairness, transparency, values alignment, AI safety, etc. The course should be accessible to anyone with an introductory background in machine learning, such as would be acquired in CMSC 422, INST 737, or equivalent.