Abstract: Getting the full benefit of a machine learning model can be difficult, and getting users to leverage and adopt it can be even more so. Although we can turn data into forecasts and insights, these reveal what’s happened in the past and what’s likely to happen next. This can still leave users asking the most important question: What should we do? For that, we need help from optimization to give business users the tools to take full advantage of our machine learning models.
When your decisions involve complex trade-offs among various business objectives and have an astronomical number of viable solutions, only mathematical optimization has the power to find the best or optimal solution – which can be used to make optimal business decisions.
The analytical journey from data to decisions may involve adding a new skill to your analytical toolbox
Join session, for a discussion on:
· When and why to ask your business user, ""What are you going to do with these results?""
· Identifying mathematical optimization problems and the main components
· How they can complement your machine learning models
· A combined data science and optimization python example and demo with Gurobi
Bio: Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.
Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS.
Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs.
Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.