Held under the aegis of Max Planck Institute for Intelligent Systems, this year's Machine Learning Summer School (MLSS) will take place in Moscow from August 26 to September 6, 2019. This is the first time that the famous international event is held in Russia, with Skolkovo Institute of Science and Technology (Skoltech) serving both as the co-organizer and the hosting venue. Registrations are open now.
The Machine Learning Summer School is a series of schools started by Max Planck Institute for Intelligent Systems in Tubingen, Germany, in 2002. Its organizers, prominent scientists B. Scholkopf and A. Smola, noticed that while many students are keen to learn about machine learning, only few machine learning courses are taught at universities. Since then, 36 editions of MLSS have taken place in different countries all over the world, namely in Europe, Asia and the Americas. For the first time ever, in 2019 the Machine Learning Summer School is coming to Eastern Europe.
"Over the course of the MLSS, students from all over the globe attend lectures and workshops on topical issues, developments and methods in the field of machine learning. These are given by leading international experts. The MLSS is the most famous and globally recognized school on machine learning, and hosting it will be a momentous event for Russia," says Evgeny Burnaev, associate professor at Skoltech and coordinator of the Moscow MLSS.
The 2019 MLSS will take place at the new campus of Skoltech Institute of Science and Technology. The program includes 14 lectures and workshops by world-renowned experts, as well as a poster session and presentations on current advances in machine learning, given by representatives of the industry's top companies.
Application deadline for participants is May 31, 2019.
The working language of the school is English.
ParticipantsMaster's and PhD students, research staff and industry members with relevant experience and interests are welcome to apply.
- Skolkovo Institute of Science & Technology
- ADASE Group
- Center for Computational and Data-Intensive Science and Engineering