The subject of this book centres
around trustworthy machine learning under imperfect data. It is primarily designed for
scientists, researchers, practitioners, professionals, postgraduates and
undergraduates in the
field of machine learning and artificial intelligence. The book focuses
on trustworthy deep learning under various types of imperfect data, including
noisy labels, adversarial examples, and out-of-distribution data. It covers
trustworthy machine learning algorithms, theories, and systems.
The main goal of the book is to provide students and researchers in academia with an
unbiased and comprehensive literature review. More importantly, it aims to stimulate
insightful discussions about the future of trustworthy machine learning. By engaging the audience
in more in-depth conversations, the book intends to spark ideas for addressing core
problems in this topic. For example, it will explore how to build up benchmark datasets in
noisy-supervised learning, how to tackle the emerging adversarial learning, and
how to tackle out-of-distribution detection.
For practitioners in the industry,
this book will present state-of-the-art trustworthy machine learning methods to
help them solve real-world problems in different scenarios, such as online
recommendation and web search. While the book will introduce the basics of
knowledge required, readers will benefit from having some familiarity with
linear algebra, probability, machine learning, and artificial intelligence. The
emphasis will be on conveying the intuition behind all formal concepts,
theories, and methodologies, ensuring the book remains self-contained at a high
level.
