LearningScale 2014 - MADALGO Summer School on LEARNING AT SCALE

MADALGO Summer School on
LEARNING AT SCALE
August 11- 14, 2014, Aarhus University, Denmark
http://madalgo.au.dk/learningatscale2014
OVERVIEW AND GOAL
The MADALGO Summer School 2014 will introduce attendees to the latest developments in learning at scale. The topics will include high dimensional inference, algorithmic perspectives on learning and optimization, and challenges in learning with huge data.
LECTURES
The school will be taught by experts in learning:
* Amr Ahmed (Google)
* Mikhail Belkin (Ohio State)
* Stefanie Jegelka (Berkeley)
* Ankur Moitra (MIT)
PARTICIPATION
The summer school will take place on August 11-14, 2014 at Center for Massive Data Algorithmics (MADALGO) at the Department of Computer Science, Aarhus University, Denmark. The school is targeted at graduate students, as well as researchers interested in an in-depth introduction to Learning. Registration will open soon at the school webpage. Registration is free on a first-come-first serve basis - handouts, coffee breaks, lunches and a dinner will be provided by MADALGO and Aarhus University.
ORGANIZING COMMITTEE
* Suresh Venkatasubramanian (University of Utah)
* Peyman Afshani (MADALGO, Aarhus University)
* Lars Arge (MADALGO, Aarhus University)
* Gerth S. Brodal (MADALGO, Aarhus University)
* Kasper Green Larsen (MADALGO, Aarhus University)
LOCAL ARRANGEMENTS
* Trine Ji Holmgaard (MADALGO, Aarhus University)
* Katrine Østergaard Rasmussen (MADALGO, Aarhus University)
ABOUT MADALGO
Center for Massive Data Algorithmics is a major basic research center funded by the Danish National Research Foundation. The center is located at the Department of Computer Science, Aarhus University, Denmark, but also includes researchers at CSAIL, Massachusetts Institute of Technology in the US, and at the Max Planck Institute for Informatics and at Frankfurt University in Germany. The center covers all areas of the design, analysis and implementation of algorithms and data structures for processing massive data (interpreted broadly to cover computations where data is large compared to the computational resources), but with a main focus on I/O-efficient, cache-oblivious and data stream algorithms.