APPLICATIONS NOW OPEN FOR
the 29th MACHINE LEARNING SUMMER SCHOOL
at Kyoto University, Japan,
23 August to 4 September 2015.
*** http://www.i.kyoto-u.ac.jp/mlss15 ***
Building upon the great success of the MLSS'12 in Kyoto (~300 participants from 50 countries, ~20 lecturers, ~75 hours of lectures), we are organizing another Machine Learning Summer School this summer, to be held again in Kyoto University, Japan,
from August 23 to September 4.
This edition will be the 29th in the now longstanding MLSS series. (http://mlss.cc)
Please share with your colleagues and students this fantastic opportunity to:
- learn from world-renowned machine learning specialists,
- network with a diverse and formidable audience,
- discover and enjoy Kyoto, one of the most beautiful cities in the world! (http://kyoto.travel/en)
We provide below an overview of the MLSS program and application process. More detailed information is available on our website: http://www.i.kyoto-u.ac.jp/mlss15
We hope to see you in Kyoto this summer!
M. Cuturi (Kyoto U.), A. Yamamoto (Kyoto U.), M. Sugiyama (U. of Tokyo)
The machine learning summer school provides advanced-undergraduate
and graduate students, industry professionals and academics of all levels
with an intense learning experience on the theory and applications of modern
Over the course of two weeks, a panel of internationally renowned lecturers
will offer tutorials covering basic as well as advanced topics.
The summer school will allow the participants to get in touch with international experts
in this field. Joint publications, new research projects and exciting opportunities will
arise from these interactions.
Confirmed Speakers and Topics
Stephen P. Boyd, Stanford
Emmanuel Candès, Stanford
Topics in High-Dimensional Statistics
Zaid Harchaoui, NYU/INRIA
Machine Learning for Computer Vision
Stefanie Jegelka, MIT
Submodular Functions in Machine Learning
Gábor Lugosi, Pompeu Fabra
Concentration Inequalities for Machine Learning
Luc de Raedt, KU Leuven
Philippe Rigollet, MIT
Statistical and Computational Aspects of High-Dimensional Learning
Lorenzo Rosasco, MIT / Genoa
Alexander J. Smola, CMU
Scalable Machine Learning
Taiji Suzuki, Tokyo Tech
Csaba Szepesvári, U. of Alberta
Ryota Tomioka, TTI Chicago
Tensor Decompositions in Machine Learning
Large Scale Deep Learning
Martin Wainwright, Berkeley
Statistical Guarantees in Optimization
Who Can Apply?
Anyone can apply from January 22 to April 10: the summer school is targeted for students (specially at a master/PhD level), academics (faculty, researchers and postdoctoral researchers) and professionals looking to use, or already using machine learning methods in their work.
This school is suitable for all levels, both for people without previous knowledge in Machine Learning, and those wishing to broaden their expertise in this area.
Student applicants (and students only) can apply for financial support to cover their trip expenses. Financial support will be given in priority to students who would not be able to attend the summer school without financial help or a registration fee waiver, despite having an excellent academic background and a previously demonstrated interest in machine learning or any related discipline. The limited support funds we have will be allocated on a competitive basis, upon reviewing application documents.
Applicants will be asked to submit a CV, a cover letter, and, for student applicants only,
a short letter of recommendation (to be submitted electronically) from one referee of their choice.
Participants are encouraged to discuss their own work with their peers and the speakers.
Applicants are thus invited to provide the title/abstract of a poster they would like to
present at the school.
Please apply here: http://www.iip.ist.i.kyoto-u.ac.jp/mlss15/doku.php?id=application
Application Opens: January 22 (NOW!)
Application Deadline: April 10.
Acceptance notification: April 24.
Registration Fees Payment Deadline: May 12.
Summer School Dates: August 23 (Sun.) - September 4 (Fri.)
For inquiries, please contact: