This document discusses machine learning and its impact on business decision making. It defines machine learning as constructing algorithms that can analyze and learn from data to make predictions. The document contrasts hypothesis-driven analytics, which starts with a business question, versus data-driven analytics, which starts by analyzing patterns in data. It provides examples of how machine learning could be applied to issues like reservation cancellations, home auctions, and encouraging altruistic behavior. The closing remarks discuss the future of machine learning and the need for machines to become more human-centric to work with people.