#30 - 2020.03 - Explainable AI and Interpretable ML
Details
🆂🅻🅾🆃 #1: Interpretable Machine Learning: How to make black box model explainable - Alexander Engelhardt
📑Complex machine learning models make better predictions, but at the cost of turning into an unexplainable black-box model. In this talk, we'll look into some techniques that allow us to explain why a model makes a specific prediction.
👨🏻🔬Alex is a freelance Data Scientist based in Munich. His work focuses on developing custom-made statistical algorithms and Machine Learning on structured data. He studied statistics and moved towards Machine Learning during his PhD studies, where he studied problems in cancer genomics and bioinformatics.
🆂🅻🅾🆃 #2: Introduction to Explainable AI on Google Cloud -
Sara Robinson, Google
📑 We often trust our high-accuracy ML models to make decisions for our users, but it’s hard to know exactly why or how these models came to specific conclusions. Explainable AI provides a suite of tools to help you interpret your ML model’s predictions. In this talk, we’ll look at how we can use Explainable AI to interpret image, text, and tabular models. You'll learn how to use Explainable AI with Cloud AI Platform, AutoML Tables, and the What-if Tool - an open source visualization tool for optimizing your ML model’s performance and fairness.
👩🏻🔬Sara is a Developer Advocate on Google's Cloud Platform team, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Sara has a bachelor’s degree from Brandeis University. Before Google, she was a Developer Advocate on the Firebase team. When she's not programming she can be found on a spin bike, listening to the Hamilton soundtrack, or eating froyo.
📌As always, we are super happy to have you with a lightning talk on your small or big successes, open source projects or just something amazing you would like to share.
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