Using ML to optimize business outcomes

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times.
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.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

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Please join us at the session that is best suited to your time zone. Note that this topic is:
1. Repeated at two different times to accommodate various time zones, because it is
2. Posted simultaneously in multiple meetup groups world-wide
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It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r35a4b43d09dd200b049cf51dd4fce7c5