Understanding how intelligence works and how it can be emulated by 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, software that is 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.
RegML is a 20 hours advanced machine learning
course including theory classes and practical laboratory sessions. 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 respects the course is
a compressed version of the 9.520 course at MIT.
The course started in 2008 has seen an increasing national and
international attendance over the years, with a peak of over 90
participants in 2014.
- application deadline: March 20
- notification of acceptance: March 27
- registration fee deadline: April 17
- students and postdocs: EUR 50
- professors: EUR 100
- professionals: EUR 150
- UNIGE students and IIT affiliates: no fee
Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment.
The registration fee is non-refundable.