Madeleine Udell
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Assistant Professor
Cornell |
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Madeleine Udell is Assistant Professor of Operations Research and
Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. She studies optimization and machine learning for large scale data analysis and control, with applications in marketing, demographic modeling, medical informatics, engineering system design, and automated machine learning. She has received several awards, including a National Science Foundation CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), a Cornell Engineering Research Excellence Award (2020), an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019), and INFORMS Doing Good with Good OR (2018). Her work is supported by grants from the NSF, ONR, DARPA, the Canadian Institutes of Health, and Capital One. |
Workshop: Automating Machine Learning
Prerequisite: Supervised learning (classification, regression); cross-validation
How should we choose a machine learning model? What kind of model (linear, tree ensemble, deep neural network) would work best? What values should you pick for model hyperparameters - will the defaults work, or should you tweak them? Have you wrung all the value out of your data, or could you predict better if only you found the right model? In this workshop we'll survey some interesting strategies for automated machine learning that can answer these questions for you.
How should we choose a machine learning model? What kind of model (linear, tree ensemble, deep neural network) would work best? What values should you pick for model hyperparameters - will the defaults work, or should you tweak them? Have you wrung all the value out of your data, or could you predict better if only you found the right model? In this workshop we'll survey some interesting strategies for automated machine learning that can answer these questions for you.