Lectures
You can download the lectures here. We will try to upload lectures prior to their corresponding classes.
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Lecture 2 - Data challenges
tl;dr: Common challenges in data collection.
[slides]
Suggested Readings:
- Lessons from measurement [only need to read measurement section]
- When You Hear the Margin of Error Is Plus or Minus 3 Percent, Think 7 Instead
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Lecture 4 - Weighting 2
tl;dr: More on weighting, Uncertainty quantification with selection on unknown covariates
[slides]
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Guest 1 - Dhrumil Mehta Guest Lecture
tl;dr: Dhrumil Mehta (Columbia and http://fivethirtyeight.com/).
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Lecture 6 - Recommendations introduction
tl;dr: Introduction to Recommendations, including data challenges and collaborative filtering
[slides]
Suggested Readings:
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In class discussion - Ethics in (Algorithmic) Pricing
tl;dr: What makes a pricing decision unfair?
[Pre-class Homework]
Suggested Readings:
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NO CLASS -- FALL BREAK
tl;dr:
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Guest 2 - Elizabeth Bondi Guest Lecture
tl;dr: Elizabeth Bondi (University of Michigan/MIT).
Virtual lecture! Zoom link on calendar.
Title: Navigating Uncertainty and Human-Agent Interactions in Multi-Agent Systems for Social Impact
Abstract: AI is now being applied widely in society, including to support decision-making in important, resource-constrained efforts in conservation and public health. Such real-world use cases introduce new challenges, like noisy, limited data and human-in-the-loop decision-making. I show that ignoring these challenges can lead to suboptimal results in AI for social impact systems, which has led me to holistically model such systems to improve results. In addition to modeling such real-world efforts holistically, I believe we must also work with all stakeholders in this research, including by making our field more inclusive through efforts like my nonprofit, Try AI.
Bio: Elizabeth Bondi-Kelly is currently a Postdoctoral Fellow at MIT through the CSAIL METEOR Fellowship and an incoming Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan. She has a PhD in Computer Science at Harvard University, where she was advised by Prof. Milind Tambe. Her research interests include multi-agent systems, remote sensing, computer vision, and machine learning, especially applied to conservation and public health. She has also founded Try AI, a nonprofit devoted to increasing diversity, equity, and inclusion in the field of AI.
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CANCELLED CLASS
tl;dr:
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Guest 3 - Sven Schmit Guest Lecture
tl;dr: Sven Schmit (Eppo).
Title: Online experimentation, a practical guide
Abstract: Nothing beats a thorough understanding of the theoretical underpinnings of experimentation. Nonetheless, there are tricks, traps, and rules of thumb that will help you succeed in practice. This talk focuses on sharing lessons from the field that often do not make it to the statistics books. The goal is to make you feel like a seasoned practitioner, even if you have not (yet) run your first experiment.
Bio: Sven Schmit is head of statistics engineering at Eppo, a start-up building an experimentation platform for the modern data stack. Previously, he worked on recommendation systems and experimentation with interference at Stitch Fix. Sven obtained his PhD at Stanford focusing on understanding how machine learning systems behave when interacting with humans, and originally hails from the Netherlands.
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CANCELLED CLASS
tl;dr:
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Guest 4 - Emma Pierson Guest Lecture
tl;dr: Emma Pierson (Cornell Tech).
Title: Data Science for Social Equality
Abstract: Our society remains profoundly unequal. This talk discusses how data science and machine learning can be used to facilitate more equitable decision-making by presenting case studies around policing and COVID-19.
Bio: Emma Pierson is an assistant professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and a computer science field member at Cornell University. She holds a secondary joint appointment as an Assistant Professor of Population Health Sciences at Weill Cornell Medical College. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by best paper, poster, and talk awards, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. Her research has been published at venues including ICML, KDD, WWW, Nature, and Nature Medicine, and she has also written for The New York Times, FiveThirtyEight, Wired, and various other publications.
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Guest 5 - Yixin Wang Guest Lecture
tl;dr: Yixin Wang (University of Michigan).
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Project work day
tl;dr: In-person project work day
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