Explainable AI - Virtual Meetup (Hebrew)

Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. In this webinar we will learn the theory and practice of SHAP (SHapley Additive exPlanations) which one of the best methods to explain individual predictions. Speakers Bio: Adi Watzman, Data Scientist at PayPal I am a data scientist at PayPal, developing machine learning models for fraud detection. I hold a BSc and an MSc in Computer Science from HUJI and Weizmann Institute respectively. I love data and algorithms, and extremely excited about using ML explainability to improve our models’ and their usage. Sireesha Muppala is an AI/ML Specialist Solutions Architect at AWS, providing guidance to customers on architecting and implementing machine learning solutions at scale. She has over 23 years of IT industry experience across multiple business verticals including Health Care, e-Insurance, Data Center Resource Management, Gift Card Processing and Infrastructure Management. While working full-time, Sireesha earned her Ph.D in May 2013 and Post Doctorate in 2015 from University of Colorado, Colorado Springs. She led the Colorado University team in winning and successfully completing a 2-year research grant from Air Force Research Lab on “Autonomous Job Scheduling in Unmanned Aerial Vehicles”. She is a published author with several of her technical research papers presented at International Conferencesand published in top tier Technical journals. She is author of multiple AWS Machine Learning blogs and the recently published “Machine Learning Lens – Well Architected” that provides guidance on building and operating ML workloads on AWS. Don't miss our future events! Join the the AWS Floor28 Tel Aviv technical community: https://floor28.co.il/ Subscribe to AWS Online Tech Talks On AWS: https://www.youtube.com/@AWSOnlineTechTalks?sub_confirmation=1 Follow Amazon Web Servi