WEBINAR / EVENT

[Podcast] Data Science Based Decisions: Mixed-Integer Programming

Learn how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.

November 07 2019

WEBINAR / EVENT

[Podcast] Data Science Based Decisions: Mixed-Integer Programming

Learn how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.

November 07 2019

WEBINAR / EVENT

[Podcast] Data Science Based Decisions: Mixed-Integer Programming

Learn how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.

November 07 2019

This latest Data Science Central podcast traces the history of mixed-integer programming (MIP), noting several parallels with machine learning. In the process, we briefly discuss the impact that MIP has had on a number of application domains and why this powerful technology should be a part of every data scientist’s analytics toolkit.

Hosted by:

Rafael Knuth, Contributing Editor – Data Science Central

This latest Data Science Central podcast traces the history of mixed-integer programming (MIP), noting several parallels with machine learning. In the process, we briefly discuss the impact that MIP has had on a number of application domains and why this powerful technology should be a part of every data scientist’s analytics toolkit.

Hosted by:

Rafael Knuth, Contributing Editor – Data Science Central

This latest Data Science Central podcast traces the history of mixed-integer programming (MIP), noting several parallels with machine learning. In the process, we briefly discuss the impact that MIP has had on a number of application domains and why this powerful technology should be a part of every data scientist’s analytics toolkit.