Statistics at UC Berkeley

Speaker: Ben Gum, AXA Rosenberg (Speaker - Featured)
Sep 26, 2017 11:00am 639 Evans Hall
Abstract:
We begin with a survey of machine learning techniques and applications outside of finance. Then we discuss our use of Machine Learning techniques at Rosenberg. Finally, we explore some alternative data sources.
Max Tegmark, Massachusetts Institute of Technology
Sep 26, 2017 3:30pm 430-8 Wozniak Lounge Soda Hall
Abstract:
How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing...
A talk by Max Tegmark
Michael I. Jordan, Statistics & EECS, UC Berkeley (Speaker)
Sep 26, 2017 4:10pm 190 Doe Library
Abstract:
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in Data Science is apparent from their sharply divergent nature at an elementary level---in computer...
On Computational Thinking, Inferential Thinking and Data Science
Mark Rudelson, University of Michigan
Sep 27, 2017 3:10pm 1011 Evans Hall
Abstract:
Consider an n by n linear system Ax=b. If the right-hand side of the system is known up to a certain error, then in process of the solution, this error gets amplified by the condition number of the matrix A, i.e. by the ratio of its largest and smallest singular values. This observation led von Neumann and his collaborators to consider the condition number of a random matrix and conjecture that...
Stefanie Jegelka, Massachusetts Institute of Technology
Sep 27, 2017 4:00pm 1011 Evans Hall
Abstract:
Discrete Probability distributions with strong negative dependencies (negative association) occur in a wide range of settings in Machine Learning, from probabilistic modeling to randomized algorithms for accelerating a variety of popular ML models. In addition, these distributions enjoy rich theoretical connections and properties. A prominent example are Determinantal Point Processes. In this...

Statistics at UC Berkeley: We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census. We have forged strong interdisciplinary links with other departments and areas of study, particularly biostatistics, mathematics, computer science, and biology, and actively seek to recruit graduate students and faculty who can help to build and maintain such links. We also offer a statistical consulting service each semester.