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Trustworthy Machine Learning under Imperfect Data

By (author) Bo Han, By (author) Tongliang Liu

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normal price: R 7 420.95

Price: R 7 049.95

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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 theÂ 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 anÂ unbiased and comprehensive literature review. More importantly, it aims to stimulateÂ insightful discussions about the future of trustworthy machine learning. By engaging the audienceÂ in more in-depth conversations, the book intends to spark ideas for addressing coreÂ problems in this topic. For example, it will explore how to build up benchmark datasets inÂ noisy-supervised learning, how to tackle the emerging adversarial learning, andÂ how to tackle out-of-distribution detection. For practitioners in the industry,Â this book will present state-of-the-art trustworthy machine learning methods toÂ help them solve real-world problems in different scenarios, such as onlineÂ recommendation and web search. While the book will introduce the basics ofÂ knowledge required, readers will benefit from having some familiarity withÂ linear algebra, probability, machine learning, and artificial intelligence. The emphasis will be on conveying the intuition behind all formal concepts,Â theories, and methodologies, ensuring the book remains self-contained at a highÂ level. Â

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