Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems.
In this latest Data Science Central webinar, you will learn:
The main components of an MO problem.
How to formulate an MO model.
How to build an MO model using the Gurobi Python API.
How to modify the original model formulation to accommodate changing conditions.
How to implement changes in the model using the Gurobi Python API.
Pano Santos, Senior PhD, Senior Technical Manager – Gurobi Optimization
Sean Welch, Host and Producer – Data Science Central