Human-In-The-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI
Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Robert (Munro) Monarch 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.