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.
Statistics at UC Berkeley
Oct 5, 2016
TBD (Speaker - Featured)
Speaker: John Arabadjis, State Street (Speaker - Featured)
Jing Lei, Department of Statistics, CMU
Cross-validation is one of the most popular model selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, unless the ratio between the training and testing sample sizes is very small. We argue that such an overfitting tendency of cross-validation is due to the ignorance of the uncertainty in the testing...
Apr 26, 2017 4:00pm ☞ 125 Li Ka Shing Center
Title: Complex traits and simple systems
Dr. Anne Carpenter, Broad Institute of Harvard and MIT
Mikhail Belkin, Department of Computer Science and Engineering, Ohio State University
What can we learn from big data? First, more data allows us to more precisely estimate probabilities of uncertain outcomes. Second, data provides better coverage to approximate functions more precisely. I will argue that the second is key to understanding the recent success of large scale machine learning. A useful way of thinking about this issue is that it is necessary to use many more...