Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI (Robert Monarch)
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Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Series: AI Books · Book deep dive Book: Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI by Robert (Munro) Monarch. Robert Monarch explains how human judgment and machine learning can work together to build better models. Active learning uses uncertainty and diversity sampling to select examples that most need human feedback. The book examines how to recruit annotators, write instructions, and measure agreement and annotation quality. It connects reliable training and evaluation data with transfer learning and other ways to improve labeling workflows. Annotation interfaces and human-computer interaction bring these methods together in practical machine learning products. Source: Manning Publications · 2021 · 424 pages · ISBN 9781617296741. Explore: AI & Computer Science · AI Books · AI Papers Related episodes: Learning Representations by Back-Propagating Errors (Rumelhart, Hinton & Williams) · Machines That Think Like People (Lake et al.)
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