|
||||||||||||||||||||||||
Course
organized within the PhD Program in Computer Science for
the
PhD
School in Sciences and Technologies for Information and
Knowledge (STIC)
PhD School in Life and Humanoid Technologies |
||||||||||||||||||||||||
Dates and registration | ||||||||||||||||||||||||
The course will be held on June, 3-7 2013 Registration for the course is closed. |
||||||||||||||||||||||||
Course at a Glance | ||||||||||||||||||||||||
Regularization Methods
for High Dimensional Learning (RegML) is a 20 hours course
including practical laboratory session. 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. The course is
co-organized by the SLIPGURU
group at the University
of
Genova and the IIT@MIT
Lab, a joint lab between the Istituto
Italiano
di Tecnologia (IIT) the Massachusetts
Institute
of Technology (MIT). |
||||||||||||||||||||||||
Instructors | ||||||||||||||||||||||||
Francesca
Odone -- University
of Genova, francesca.odone@unige.it
Lorenzo Rosasco -- Istituto Italiano di Tecnologia (IIT) and Massachusetts Institute of Technology (MIT). , lrosasco@mit.edu |
||||||||||||||||||||||||
Venue | ||||||||||||||||||||||||
The course will be held
at the Department of
Informatics Bioengineering Robotics and Systems Engineering
(DIBRIS)
of the University of Genova in Via Dodecaneso 35, 16146
Genova. When looking for
directions keep this address in mind, since DIBRIS has
multiple locations (non so close to one another). Here
you can find directions and travelling information. Here you can find a list
of hotels near the department (~ 20' walk) or in the
city centre (~20' by bus). NEW!!Here
is a list of places where you can
go for lunch. And here is a link to the online google
map
Genova is in the region of Liguria in the Italian
Riviera (see here
or here
for some nice pics and a video)
|
||||||||||||||||||||||||
Synopsis | ||||||||||||||||||||||||
Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can ?see? and detect pedestrians and ATM machines 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. Among the variety of
approaches to modern computational learning, we focus on
regularization techniques, that are key to high-
dimensional learning. Regularization methods allow to treat in
a unified way a huge class of diverse approaches, while
providing tools to design new ones. Starting from classical
notions of smoothness, shrinkage and margin, the course will
cover state of the art techniques based on the concepts of
geometry (aka manifold learning), sparsity and a variety of
algorithms for supervised learning, feature selection,
structured prediction, multitask learning and model selection.
Practical applications for high dimensional problems will be
discussed. ?The classes will focus
on algorithmic and methodological aspects, while trying to
give an idea of the underlying theoretical underpinnings.
Practical laboratory sessions will give the opportunity to
have hands on experience. 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.
|
||||||||||||||||||||||||
Syllabus | ||||||||||||||||||||||||
- each
class is 90 min. no breaks - |
||||||||||||||||||||||||
Schedule and rooms | ||||||||||||||||||||||||
- room 322 (sala conferenze) - 3rd floor |
||||||||||||||||||||||||
Credits
and Exam (optional) |
||||||||||||||||||||||||
If
you attend most of the classes you
will be attributed 2 credits
(according to the ECTS grading scale). The
credits attribution
will be reported on the certificate
of attendance
we will handle
at the end of the course. If you need an evaluation the exam will consist in a brief report (~ 5 pages + 1 page of figures) of the labs. Submission deadlines: 15/09 and 01/12/2013. Submit your report (one or multiple authors are fine) by sending an email to both Francesca and Lorenzo and specifying the type of evaluation you need (eg., passed / ranking / marking...) |
||||||||||||||||||||||||
Prerequisites | ||||||||||||||||||||||||
Multivariate Calculus, Basic Probability Theory, Matlab. | ||||||||||||||||||||||||
Short reading list | ||||||||||||||||||||||||
General references are
|
||||||||||||||||||||||||
Photos ... spot the differences! | ||||||||||||||||||||||||
|
||||||||||||||||||||||||
|