This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.