Explore a comprehensive conference recording from the ACM-IMS Interdisciplinary Summit on the Foundations of Data Science, held in San Francisco on June 15, 2019. Delve into cutting-edge topics in data science through keynote talks, panel discussions, and expert presentations. Learn about making black box models effective, deep learning applications, reinforcement learning, robustness and stability in data science, fairness and ethics, and the future of the field. Gain insights from renowned experts including Emmanuel Candès, Jeffrey Dean, Daphne Koller, and many others as they discuss critical issues and advancements in data science. Benefit from nearly six hours of in-depth content covering a wide range of topics, from statistical methods to machine learning applications in drug discovery.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
– Introduction
– Keynote Talk: "Making the Black Box Effective: What Statistics Can Offer"
– Panel: Deep Learning, Reinforcement Learning, and Role of Methods in Data Science
– Panel: Robustness and Stability in Data Science part 1
– Panel: Robustness and Stability in Data Science part 2
– Panel: Fairness and Ethics in Data Science Moderator: Yannis Ioannidis, National and Kapodistrian University of Athens
– Keynote Talk: "Deep Learning for Tackling Real-World Problems"
– Keynote Talk: "Machine Learning: A New Approach to Drug Discovery" Daphne Koller, insitro, with introduction by Kristian Lum, Human Rights Data Analysis Group
– Panel: Future of Data Science Moderator: David Madigan, Columbia University; Panelists: Michael I. Jordan, University of California, Berkeley, Jeannette Wing, Columbia University
Taught by
Association for Computing Machinery (ACM)
