Trustworthy Machine Learning under Imperfect Data

The subject of this book centresaround trustworthy machine learning under imperfect data. It is primarily designed for
scientists, researchers, practitioners, professionals, postgraduates andundergraduates in the
field of machine learning and artificial intelligence. The book focuseson trustworthy deep learning under various types of imperfect data, includingnoisy labels, adversarial examples, and out-of-distribution data. It coverstrustworthy machine learning algorithms, theories, and systems.