How machine learning meets optimization
Who is this presentation for?People who provides solutions for complex analytics problems in Finance, Manufacturing, Transport, Healthcare and more.
Convergence of Optimization and ML: A powerful combination.
Optimization and ML are increasingly intersecting. Occasionally overlapping, sometimes complementary and most often best used in combination. Data Scientists should be interested in Operations Research, and Operations Researcher’s are increasingly using Machine Learning. This presentation will provide a structure for understanding and applying these two techniques. It describes when Optimization techniques originating from operations research are the better solution and when it is beneficial to apply ML. More importantly, we describe how complex, high-value business problems can be solved with better by combining the techniques, rather than by using only one of them. People struggle to describe how they relate from a theoretical perspective; Is ML just an optimization problem? How does Simplex, MIP, Interior point, neural networks, and gradient descent relate?. We will outline a model that helps the understanding of how the two techniques related, overlap and differ.
Prerequisite knowledgeLimited knowledge of statistical, ML, And optimization helps but not required. If you don't know ML you will get the basics of Opti and vise versa.
What you'll learn
VP of Technology
Jari holds a Ph.D. in Computer Science from the Royal Institute of Technology, Stockholm Sweden. Dr. Koister has also been teaching at the Data Science program at UC Berkeley.
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