An overview of machine learning interpretability

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CIC Cambridge

245 Main St · Cambridge, MA

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Floor 3, Room Mosaic

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Speaker: Mehrnoosh Sameki, Technical Program Manager at Microsoft

An overview of machine learning interpretability

6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments
6:30pm - 7:20pm - Talk
7:20pm - 7:30pm - Q&A
7:30pm - 8:00pm - Networking

Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her Ph.D. degree in computer science at Boston University.

With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a ‘black box’, harder to understand and interpret. To gain the user’s trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. Data scientists also need to have the necessary insights to learn how the model can be improved. Much research has gone into model interpretability and recently several open sources tools, including LIME, SHAP, and GAMs, etc., have been published on GitHub. In this talk, we present popular state-of-the-art interpretability algorithms and introduce a Machine Learning Interpretability toolkit which incorporates the cutting-edge technologies in the domain of AI transparency. Using this toolkit, data scientists can explain machine learning models using state-of-art technologies in an easy-to-use and scalable fashion.

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