Mathematical optimization and machine learning are two tools that, at first glance, may seem to have a lot in common.

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...

The Graduate School of Information Science (GSIS) at Tohoku University, together with the Physics and Informatics (PHI) Lab ...

As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed ...

In an era where AI decides which brands get surfaced, summarized or cited, the question isn't "How can I rank higher?" It is ...

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

The Department of Industrial Engineering and Management Sciences is a leader in the science of decision-making in complex environments through innovation in algorithms, computation, and mathematical ...

For centuries, mathematicians have tried to prove that Euler’s fluid equations can produce nonsensical answers. A new approach to machine learning has researchers betting that “blowup” is near.

The design of chiral photonic structures using two machine learning methods for rapid optimization of optical properties for dielectric metasurfaces.

The International Conference in Optimization and Learning (OLA2024), organized by RIT (Croatia) and the University of Lille (France), focuses on the future challenges of optimization and learning ...