PhD-Summer Course- June 30th- July 4th, Genova, Italia
hours advanced machine learning course including theory classes
and practical laboratory session. The course covers foundations as
well as recent advances in Machine Learning with emphasis
on high dimensional data and a core set techniques, namely
regularization methods. In many respect the course is compressed version
of the 9.520 course at MIT
course started in 2008 has seen an increasing national and international
attendance over the years with a peak of 85 participants in
CLOSED, COURSE ACTIVATION CONFIRMED!
We will not accept further registrations since we have reached a
maximum number. If you have registered, you will soon receive
a confirmation e-mail.
here list of
participants, let us know if your information are not correct.
NOTE: the course has no
registration fee but participants need to take care of their
accommodations -- see below for a list of hotels.
Basic Info | Synopsis | Syllabus | More Info
In collaboration with:
Venue: Classes will take place at the Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) of the University of Genova in Via Dodecaneso 35, 16146 Genova. See here for directions and travelling information
Genova is in the region of Liguria in the Italian Riviera (see here or here for some nice pics and a video)
Accomodations: Here you can find a list
of hotels near the department (~ 20' walk) or in the city centre
(~20' by bus).
Lunch: Here is a list of places where you can go for lunch. And here is a link to the online google map
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, in particular in computational vision, 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.
- each class is 90 min. no breaks -
Welcome+ Introduction to Learning
|11:30pm:1pm||2||Kernels, Dictionaries, and Regularization|
|2:30pm:4 pm||3||Regularization Networks and Support Vector Machines|
||9:30am-11am||4||Spectral methods for supervised learning|
|Tue||2:30pm:4pm||6||Lab 1 - Binary classification and model selection|
||9:30am-11am||7||Error Analysis and Parameter Choice|
|Wed||11:30am-1pm||8||Lab 2 - Spectral filters and multi-class classification|
||Structure Sparsity and Multiple Kernel Learning|
||Lab 3 - Sparsity-based learning|
||Applications to high dimensional problems|
Credits and Exams: 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: TBA. 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: Basic Multivariate Calculus, Basic Probability Theory, Matlab.