In this talk John Myles White surveys some basic methods for analyzing data in a streaming manner. He focuses on using stochastic gradient descent (SGD) to fit models to data sets that arrive in small chunks, discussing some basic implementation issues and demonstrating the effectiveness of SGD for problems like linear and logistic regression as well as matrix factorization.
Also see SlideShare: http://www.slideshare.net/g33ktalk/john-myles-white-keynote
ABOUT THE SPEAKER: John is one of the primary developers of Julia, a new language for technical computing. He is currently developing the statistical and machine learning infrastructure for Julia. In addition, he is one of the residents at Hacker School’s Summer 2013 program.
John recently finished his Ph.D. at Princeton, where he developed models of human decision-making. During grad school, John co-wrote Machine Learning for Hackers and Bandit Algorithms for Website. Starting in the fall, John will be a research scientist at Facebook.