Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Publisher: MIT Press

Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. Dec 28, 2012 - Introduction: In a previous post we introduced the problem of detecting Gravity Waves using Machine Learning and suggested using techniques like Minimum Path Basis Pursuit. Http://www.cs.nyu.edu/~mohri/ml14/. Art authentication is an increasingly fraught field, with artist-specific foundations, collectors and experts tangled over who has the final word over attribution. May 7, 2014 - A 20 hours advanced machine learning course including theory classes and practical laboratory session. Apr 8, 2014 - All courtesy of what is called "machine learning": the ability of artificial intelligence programs to learn from their own performance working with large data sets, enabling them to do things they are not explicitly programmed to do. Papers will be assessed in a rigorous eDiscovery; Software, automation and machine learning in IP law; Trends in legal software and search engines. 2 days ago - The conference also serves as a venue for researchers to showcase their work on the theoretical foundations of computational models of law. Dec 29, 2012 - One of our first programs, set for Fall 2013, will be on the “Theoretical Foundations of Big Data Analysis”. Jan 31, 2014 - Deal: Free eBooks on Statistical Learning (in PDF), Store: , Category: Books & Magazines. Jun 19, 2009 - The workshop is on the relationship between database privacy and the theoretical foundations of statistics and machine learning. , hard copy is being sold at Amazon for $66.50 . Basically, machine learning gives at last turns its attention to art. Jun 1, 2012 - Carnegie Mellon University, Center for Formal Epistemology presents: Workshop on Foundations for Ockham's Razor All are welcome to attend. Jul 10, 2013 - The above scenarios give rise to fundamental theoretical questions in machine learning and form the basis of Boosting. Foundations of machine learning. Accordingly, authors are invited to eDiscovery and eDisclosure; Open data, linked data and big data; Machine learning; Argument mining.