Mathematical optimization, specifically Mixed Integer Programming (MIP), is a technology that is used to solve a large variety of problems within multiple industries, including supply chain planning, electrical power generation and distribution, computational finance, sports scheduling, and many more. This powerful technology is complementary to Machine Learning and should be a part of every data scientist’s analytics toolbox. In this webinar, you will learn:
We will present real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. We will also provide you with next steps on how to get started with optimization as well as available resources.
Please click here to download the presented slides.
Operational and Content Engineer
Gurobi Optimization
Operational and Content Engineer
Gurobi Optimization
Dr. Gwyneth Butera has a BA magna cum laude from Colgate University in Mathematics and a PhD in Computational and Applied Mathematics from Rice University. Her thesis work explored algorithms and heuristics for solving a class of quadratic programming problems that arise in portfolio optimization.
Principal, End-to-End Analytics
Principal, End-to-End Analytics
Dr. Halper holds a PhD in Applied Mathematics from the University of Maryland. Russell is passionate about developing actionable, pragmatic analytics that align closely with business processes to generate world-class results. He has devoted his career to developing cutting-edge analytics solutions to problems in supply chain, manufacturing, and marketing.
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