Plenary Session II

On Big Data, Optimization and Learning

In this talk I review a couple of applications on Big Data that I personally like and I try to explain my point of view as a Mathematical Optimizer — especially concerned with discrete (integer) decisions — on the subject. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. For such an integration I try to answer three questions: 1) what can optimization do for machine learning? 2) what can machine learning do for optimization? 3) which new applications can be solved by the combination of machine learning and optimization?

Location: Auditorium Date: November 28, 2017 Time: 11:00 am - 12:00 pm Andrea Lodi