Caltech president Thomas F. Rosenbaum accepts the ALS Ice Bucket Challenge from MIT president Rafael Reif. Dr. Rosenbaum gets doused in the flume in the Earth Surface Dynamics Laboratory (http://geomorph.caltech.edu...) of Michael Lamb, Caltech professor of geology. Dr. Rosenbaum challenges Worcester Polytechnic Institute president and Caltech alumna Laurie Leshin (MS '89, PhD ’95) and University of Chicago provost Eric Isaacs. The water used in the flume's experiments is recycled; it gets pumped from the flume's floor back up to the top of the incline. No water was wasted in the making of this video.
On Friday, 18 January 2013, Caltech hosted TEDxCaltech: The Brain, an exciting one-day multidisciplinary public conference, which deconstructed, deciphered, and explored some of the greatest challenges, innovations, concepts, and potentialities of the brain. It was an awe-inspiring event that brought together international scientists, innovators, entrepreneurs, and civic leaders -- along with Caltech faculty, postdocs, students, alumni, and staff -- for an exhilarating day of conversation, stimulation, and learning. Visit TEDxCaltech.com for more details.
A One Day Symposium on the Emerging Science of Big Data Visualization was held at Beckman Auditorium at Caltech on May 23, 2013, in Pasadena, CA, USA. More info: http://www.hi.jpl.nasa.gov/datavis.
Nearly every scientific and engineering endeavor faces a fundamental challenge to see and extract insights from data. Effective Data Science and Visualization can lead to new discoveries. Together, we at Caltech, NASA JPL, and Art Center represent the same convergence of science, engineering and design that drives new Big Data-powered discovery. Industry leaders came together for a series of talks to inspire, unite, and challenge our community to re-examine our practices, and our perspectives.
This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which we do not have full mathematical specification (and that covers a lot of systems). The course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
Find course materials in the iTunes U Course App - https://itunesu.itunes.apple.com/audit/CODBABB3ZC and on the CS156 website - http://work.caltech.edu/telecourse.html