The Minds of the New Machines | Research Horizons | Georgia Tech’s Research News

Georgia Tech's Research Horizons Magazine has done a very nice write-up of the ML@GT center, featuring many of our research projects. Machine learning has been around for decades, but the advent of big data and more powerful computers has increased its impact significantly — ­moving machine learning beyond pattern recognition and natural language processing into a … Continue reading The Minds of the New Machines | Research Horizons | Georgia Tech’s Research News

Ethics Highlight ‘Day of Machine Learning Discussion’ | College of Computing

Machine learning at Georgia Tech was in the spotlight recently as The Center for Machine Learning at Georgia Tech (ML@GT) hosted its spring seminar on Feb. 22 in the Klaus Advanced Computing Building.Billed as a “day of discussions around machine learning,” more than 200 students and faculty from across campus registered for the daylong event.“AI is … Continue reading Ethics Highlight ‘Day of Machine Learning Discussion’ | College of Computing

Welcome to all the new Machine Learning Faculty to Georgia Tech

We are pleased to have these new faculty join us starting this year. Siva Maguluri CoE / ISyE Devi Parikh CoC / IC Jacob Abernathy CoC / SCS Dhruv Batra CoC / IC Rachel Cummings CoE / ISyE Eva Dyer CoE / BME Wenjing Liao CoS / Math Thomas Ploetz CoC / IC Tuo Zhao … Continue reading Welcome to all the new Machine Learning Faculty to Georgia Tech

ML/Statistics Seminar by Shama Kakade on “Faster least squares and faster eigenvector computation: Improved algorithms for both optimization and statistics in the big data regime”

ML/Statistics Seminar Series Date/Time: Thursday Sep 28 2017, 11:00 am - 12:00 pmLocation: Advisory Boardroom, #402 Groseclose Speaker: Sham Kakade; Department of Statistics and Computer Science, University of Washington Title: Faster least squares and faster eigenvector computation: Improved algorithms for both optimization and statistics in the big data regime Abstract: Least squares and eigenvector computations … Continue reading ML/Statistics Seminar by Shama Kakade on “Faster least squares and faster eigenvector computation: Improved algorithms for both optimization and statistics in the big data regime”

Seminar by Nathan Silberman on “TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow” Thursday Sep 7 2017, 4:30 pm – 5:45 pm in Clough 144

ML@GT Seminar and Guest Speaker for CS 7643 Deep Learning Title: TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow Speaker: Nathan Silberman Date/Time: Thursday Sep 7 2017, 4:30 pm - 5:45 pm Location: Clough 144 Abstract: TF-Slim is a TensorFlow-based library with various components. These include modules for easily defining neural network models … Continue reading Seminar by Nathan Silberman on “TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow” Thursday Sep 7 2017, 4:30 pm – 5:45 pm in Clough 144

ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”

Machine Learning/Statistics Seminar Series Date/Time: Thursday Sep 7 2017, 11:00 pm - 12:00 pm Location: Advisory Boardroom, #402 Groseclose Speaker: Xiaodong Li Title: Convex Relaxation for Community Detection Abstract: Cluster structures are ubiquitous for large data, and community detection has recently attracted much research attention in applied physics, sociology, computer science and statistics due to … Continue reading ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”

ML/Statistics Seminar by Jason Lee on “Matrix Completion, Saddlepoints, and Gradient Descent”

ML/Statistics Seminar Series Date/Time: Wed. Sep 6 2017, 3:00 pm - 4:00 pm Location: Advisory Boardroom, #402 Groseclose Speaker: Jason Lee Title: Matrix Completion, Saddlepoints, and Gradient Descent Abstract: Matrix completion is a fundamental machine learning problem with wide applications in collaborative filtering and recommender systems. Typically, matrix completion are solved by non-convex optimization procedures, … Continue reading ML/Statistics Seminar by Jason Lee on “Matrix Completion, Saddlepoints, and Gradient Descent”