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|
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.
The course is
co-organized by the SLIPGURU
group at the University
Genova and the IIT@MIT
Lab, a joint lab between the Istituto
di Tecnologia (IIT) the Massachusetts
of Technology (MIT).
Odone -- University
of Genova, email@example.com
Lorenzo Rosasco -- Istituto Italiano di Tecnologia (IIT) and Massachusetts Institute of Technology (MIT). , firstname.lastname@example.org
The course will be held
at the Department of
Informatics Bioengineering Robotics and Systems Engineering
of the University of Genova in Via Dodecaneso 35, 16146
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).
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
?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.
class is 90 min. no breaks -
|Schedule and rooms|
- room 322 (sala conferenze) - 3rd floor
and Exam (optional)
you attend most of the classes you
will be attributed 2 credits
(according to the ECTS grading scale). The
will be reported on the certificate
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...)
|Multivariate Calculus, Basic Probability Theory, Matlab.|
|Short reading list|
General references are
|Photos ... spot the differences!|