Mathematical Optimization and Machine Learning

Mathematical optimization and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions.

For Data Scientists to be effective, an understanding of MIP and when to use it is critical, as ML does not solve all problems effectively.

Labor Strategy Optimization for the Professional Services Industry

People are the biggest asset for the professional services industry, as well as one of its biggest expenses. With the help of strategic workforce planning, a professional service firm can better allocate its available workforce to the demands of service delivery.

KPMG: Using Optimization to Cope with Uncertainty

In this webinar, KPMG will discuss how to turn disruption into an opportunity and how to use mathematical tools to make better decisions during times of uncertainty. They will deep dive into a few use cases, including the distribution of industrial goods and food.

How the FCC Uses Mathematical Optimization

Watch this short webinar to learn about how the Federal Communications Commission (FCC) uses mathematical optimization to schedule the reassignment of channels for broadcast television stations in the United States and Canada in order to free up spectrum for mobile use and 5G.

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