[image 01047] 機械学習サマースクール＠京大（2015/8/23-9/4）

[Masashi Sugiyama]

image MLの皆様

東京大学の杉山将です．
２０１５年８月２３日～９月４日に京都大学で
機械学習の国際サマースクール(MLSS2015)を開催します．
以下に示しましたように，世界的に著名な研究者が講師として登壇します．
本日より申し込みサイトがオープンしましたので，奮ってお申込み下さい．

オーガナイザ：クトゥリ マルコ（京大），山本章博（京大），杉山将（東大）

==========================================
                APPLICATIONS NOW OPEN FOR
==========================================
  the 29th MACHINE LEARNING SUMMER SCHOOL
                     in Kyoto University, Japan,
                 23 August to 4 September 2015.
           ***  http://www.i.kyoto-u.ac.jp/mlss15  ***
==========================================

Dear Colleagues,

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!
With regards

The organizers,
M. Cuturi (Kyoto U.), A. Yamamoto (Kyoto U.), M. Sugiyama (U. of Tokyo)

============================================
Scope
============================================

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
machine learning.

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
    Convex Optimization

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
    Probabilistic Programming

Philippe Rigollet, MIT
    Statistical and Computational Aspects of High-Dimensional Learning

Lorenzo Rosasco, MIT / Genoa
    Learning Representations

Alexander J. Smola, CMU
    Scalable Machine Learning

Taiji Suzuki, Tokyo Tech
    Stochastic Optimization

Csaba Szepesvári, U. of Alberta
    Reinforcement Learning

Ryota Tomioka, TTI Chicago
    Tensor Decompositions in Machine Learning

Vincent Vanhoucke, Google
    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.

============================================
Application Process
============================================

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

============================================
Important Dates
============================================

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:
mlss.kyoto.2015 ＠ gmail.com
============================================