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Course at a Glance | ||||||||||||||||||||||||||||||
We
are
glad to offer a summer graduate course on Computational
Learning.
The course covers the foundations as well as the recent
advances in
Computational Learning with particular emphasis on the
analysis of high
dimensional data and focusing on a set of core techniques,
namely
regularization methods. See the synopsis
and the syllabus for more
details. |
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Dates and registration | ||||||||||||||||||||||||||||||
The course will be held on June, 6-10 2011 Registration is free and requires sending an e-mail to one of the instructors by April 30, 2011. |
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Instructors | ||||||||||||||||||||||||||||||
Francesca
Odone -- University
of Genova,
odone@disi.unige.it Lorenzo Rosasco -- Istituto Italiano di Tecnologia (IIT) and Massachusetts Institute of Technology (MIT). , lrosasco@mit.edu |
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Venue | ||||||||||||||||||||||||||||||
The
course
will be held in the Department
of Computer Science (DISI) of
the University
of Genova (see here
for travelling information). |
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Synopsis | ||||||||||||||||||||||||||||||
Classes
will be helUnderstanding
how intelligence works and how it can be emulated in machines
has been
an elusive problem for decades and it is arguably one of the
biggest
challenges in modern science. Learning, its principles, and
computational implementations are at the very core of this
endeavor.
Only recently we have been able, for the first time, to
develop artificial intelligence systems able to
solve complex tasks that were considered out of reach for
several
decades. Modern cameras can recognize faces, and smart phones
recognize
people voice, car provided with cameras can detect pedestrians
and ATM
machines can automatically read checks. In most cases at
the
root of these success stories there are machine learning
algorithms,
that is, softwares that are trained rather than programmed
to
solve a task. Slides
of
the classes will be posted on this website and scribes of most
classes, as well as other material, can be found on the 9.520
course webpage
at MIT.
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Syllabus | ||||||||||||||||||||||||||||||
-
each class is 90 min. no breaks - |
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Course schedule and rooms | ||||||||||||||||||||||||||||||
- room 322 (sala conferenze) - DISI 3rd floor [NOTICE THERE HAS BEEN A CHANGE HERE!] - lab SW2 - DISI 3rd floor |
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Prerequisites | ||||||||||||||||||||||||||||||
Multivariate Calculus, Basic Probability Theory, Matlab. | ||||||||||||||||||||||||||||||
Short reading list | ||||||||||||||||||||||||||||||
General references are
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