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.

RegML is a 22 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 respect the course is 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.

NOTE: the course has a small registration fee from this year: 50 Eu (100 Eu for industrial attendee and no fee for students from UNIGE), read this letter for details. When accepted each candidate has to follow the instruction in the acceptance email to proceed with the payment.

Notification of acceptance: May 13.

Related courses:

• RegML 2017 website
• MIT 9.520 - Statistical Learning Theory and Applications. This is a term long course of roughly 25 lectures offered to graduate students at MIT.
• ML - Machine Learning 2017-2018. Undergraduate term-long introductory Machine Learning course offered at the University of Genova.
• MLCC - Machine Learning Crash Course. A one week (crash) course of 10 lectures, including theoretical and practical sessions.
• CBMM Summer School: Machine Learning Classes. One day introduction to the essential concepts and algorithms at the core of modern machine learning.