Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.

Key Features

·   Active Learning to sample the right data for humans to annotate

·   Annotation strategies to provide the optimal interface for human feedback

·   Supervised machine learning design and query strategies to support Human-in-the-Loop systems

·   Advanced Adaptive Learning approaches

·   Real-world use cases from well-known data scientists

For software developers and data scientists with some basic Machine

Learning experience.

About the technology

“Human-in-the-Loop machine learning” refers to the need for human interaction with machine learning systems to improve human performance, machine performance, or both. Ongoing human involvement with the right interfaces expedites the efficient labeling of tricky or novel data that a machine can’t process, reducing the potential for data-related errors.

Robert Munro has built Annotation, Active Learning, and machine learning systems with machine learning-focused startups and with larger companies including Amazon, Google, IBM, and most major phone manufacturers. If you speak to your phone, if your car parks itself, if your music is tailored to your taste, or if your news articles are recommended for you, then there is a good chance that Robert contributed to this experience.

Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.