How does a machine learn when nobody tells it what is right or wrong?

This volume is about learning when labels are scarce: discovering structure in data, grouping similar users and items, detecting anomalies, and improving decisions with feedback from the real world.

What this book will make intuitive (and rigorous)

• Unsupervised learning: clustering, representations, and structure without labels
• Recommendations: learning from clicks, views, and implicit preferences
• Learning from feedback: exploration vs. exploitation in live systems
• Evaluation in the wild: what changes when the world moves
• AI literacy: using these tools responsibly