When: December 2, 2015, 12:30 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
Machine Learning: Art or Science?
The notion of machines that can learn has caught imaginations since the days of the early computer. In recent years, as we face burgeoning amounts of data around us that no human mind can process, machines that can learn to automatically find insights from such vast amounts of data have become a growing necessity. The field of machine learning is a modern marriage between computer science and statistics, and is the soul behind what is increasingly termed “data science”. But, is machine learning a science or an art? While I won’t answer the question fully, I’ll argue that with a scientific approach, machine learning is indeed a science, and a beautiful and powerful one at that: it has rigorous mathematics at its core, its judicious use allows us to make various kinds of impact on society, and its exploration together with other natural and social sciences allows us to uncover surprising natural and social phenomena. I’ll illustrate these ideas with examples from our work on foundations of supervised learning, applications in predicting anticancer drug response in patients, and connections with social sciences in understanding how we make choices.
Bio: Shivani Agarwal is the 2015-16 William and Flora Hewlett Foundation Fellow at the Radcliffe Institute for Advanced Study at Harvard University, where she is on leave from her position as Assistant Professor and Ramanujan Fellow at the Indian Institute of Science. She leads the Machine Learning and Learning Theory Group at the Indian Institute of Science and co-directs the Indo-US Joint Center for Advanced Research in Machine Learning, Game Theory and Optimization, and is an Associate of the Indian Academy of Sciences and of the International Center for Theoretical Sciences. Prior to the Indian Institute of Science, she taught at MIT as a postdoctoral lecturer. She received her PhD in computer science at the University of Illinois, Urbana-Champaign, and her bachelor’s degrees in mathematics and computer science at St. Stephen’s College, University of Delhi and as a Nehru Scholar at Trinity College, University of Cambridge. Her research interests include foundational questions in machine learning, applications of machine learning in the life sciences, and connections between machine learning and other disciplines such as economics, operations research, and psychology.
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.