Mathematical optimization (AKA Mixed Integer Programming) 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.
In this latest Data Science Central webinar, you will hear the results of the 2019 Mathematical Optimization Survey commissioned by Gurobi and conducted by Forrester and insights on how Data Scientists can use tools such as MIP to make complex decisions.
- The latest trends in ML and Artificial Intelligence
- Key findings from the Mathematical Optimization Survey
- How you can use MIP in concert with ML techniques
- How industries are using MIP today to efficiently use resources, often resulting in time savings and millions of dollars in cost savings
All registrants will receive a copy of the 2019 Mathematical Optimization Study available in October 2019.
Mike Gualtieri, VP Principal Analyst, Application Development and Delivery Professionals - Forrester Research
Edward Rothberg, CEO and Co-Founder - Gurobi Optimization
Stephanie Glen, Editorial Director - Data Science Central