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Partner Event: Machine Learning Interpretability + Intro & Demo of LIME
May 3 @ 6:00 pm
Here’s the synopsis:
Chicago Makers – We’re back!
Join us this evening with two great talks by Patrick Hall and Dustin Verzal.
Special thanks to WeWork Fulton Market for providing the venue.
See you there!
6:00 – 6:30 – Doors open & Pizza
6:30 – 7:15 – Patrick’s Talk
7:15 – 8:00 – Dustin’s Talk
8:00 – 8:30 – Q&A & Networking
Patrick Hall’s Talk:
While understanding and trusting models and their results is a hallmark of good (data) science, model interpretability is a serious legal mandate in the regulated verticals of banking, insurance, and other industries. Moreover, scientists, physicians, researchers, analysts, and humans, in general, have the right to understand and trust models and modeling results that affect their work and their lives. Today many organizations and individuals are embracing deep learning and machine learning algorithms but what happens when people want to explain these impactful, complex technologies to one another or when these technologies inevitably make mistakes?
This talk presents several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results. The talk will include:
– Data visualization techniques for representing high-degree interactions and nuanced data structures.
– Contemporary linear model variants that incorporate machine learning and are appropriate for use in regulated industries.
– Cutting edge approaches for explaining extremely complex deep learning and machine learning models.
Wherever possible, interpretability approaches are deconstructed into more basic components suitable for human storytelling: complexity, scope, understanding and trust.
Patrick Hallis a senior data scientist and product engineer at H2O.ai. Patrick works with H2O.ai customers to derive substantive business value from machine learning technologies. His product work at H2O.ai focuses on two important aspects of applied machine learning, model interpretability, and model deployment. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.
Prior to joining H2O.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Dustin’s Talk: Model understanding for the layman:
Many of us have likely encountered friction when approaching the use of black-box models in the business world. It speaks for itself when even we, as modelers, refer to it as a black box. how do we have valuable conversations with non-experts about such a complex thing? How can we develop understanding and confidence in these models? Leave it to data scientists to layer a model on top of another model to strengthen its interpretability! We’ll explore both strengths and weaknesses of an application of LIME (Local interpretable model-agnostic explanations) in the insurance world.
Bio: Dustin Verzal is a Junior Data Scientist at Milliman, Inc. He recently graduated from the University of Wisconsin-Milwaukee where he acquired a B.S. in Computational Mathematics alongside Applied Mathematics & Computer Science (AMCS). During which he placed 2nd nationally with his team in the Milwaukee Great Lakes Great Games Hackathon. He is currently working on research in machine learning applications for the health insurance industry. When he isn’t working, Dustin spends his free time playing ultimate Frisbee (attempting not to fracture his clavicle again), writing/producing music, and DJing!
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