How machine learning meets optimization
Who is this presentation for?
- People who provide solutions for complex analytics problems in finance, manufacturing, transport, healthcare and more
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 researchers are increasingly using machine learning.
Jari Koister dives into understanding and applying these two techniques. He explores when optimization techniques originating from operations research are the better solution and when it’s beneficial to apply ML. More importantly, he outlines how complex, high-value business problems can be better solved by combining the techniques rather than by using only one of them.
People struggle to describe how they relate from a theoretical perspective, if ML is just an optimization problem, or how simplex, MIP, interior point, neural networks, and gradient decent relate. Jari outlines a model that helps you understand how the two techniques are related, overlap, and differ.
- A basic understanding of statistical, ML, and optimization (useful but not required)
What you'll learn
- Discover the difference between ML and optimization and how they're best used together
- Hear examples of applications that have greatly benefitted from the combination
Jari Koister is the vice president of product and technology at FICO. He also teaches in the Data Science Program at UC Berkeley. Previously, he has, among other things, led the development of Chatter, Salesforce’s social enterprise application and platform; founded and served as CTO at Groupswim.com, an early social enterprise collaboration company (acquired by Salesforce); founded and served as CSO and CTO at Qrodo.com, an elastic platform for broadcasting sports events live on the internet; led the development of CommerceOne’s flagship product MarketSite; and led research in computer languages and distributed computing at Ericsson Labs and Hewlett-Packard Laboratories. Jari holds a PhD in computer science from the Royal Institute of Technology, Stockholm, Sweden.
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