Mathematical Optimization + 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. 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.

--

--

Our Mission
Gurobi strives to help companies make better decisions through the use of prescriptive analytics. We provide the best math programming solver, tools for distributed optimization, optimization in the cloud, and outstanding support. We are committed to improving our solver performance and developing tools to help you use Gurobi with more ease.

Founded in 2008 by arguably the most experienced and respected team in optimization circles, we have successfully expanded to serving over 2,400 companies from a wide range of industries, by way of providing the right mix of advanced developments and technologies, world-class support and flexible licensing.

--

#optimization #datascience #dataanalytics #machinelearning #analytics #research #operationsresearch #Gurobi #gurobipy #AI #artificialintelligence #algorithms #mathematicaloptimization #jupyternotebook #heuristics #MIP #mixedintegerprogramming #MIQP

[NoOne-qshe]

Very interesting. Wish I participated in this webinar. I wonder if the speakers see a specific area or industry where a combined approach (ML & MO) has the largest yet unused potential.

[hsingkao]

Great content (but poor sound quality). Thank you 🙏