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.

This course provides an introduction to the fundamental methods at the core of modern Machine Learning. It covers foundations as well as essential algorithms. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions. The course started in 2013 has seen an increasing national and international attendance over the years with a peak of over 150 participants in 2015.

Related courses:

• MLCC 2017 website.
• RegML 2016. A one week course on more advanced regularization methods in Machine Learning. This is a short version of the MIT 9.520 course.
• 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 2016. Undergraduate term-long introductory Machine Learning course offered at the University of Genova.
• CBMM Summer School: Machine Learning Classes. One day introduction to the essential concepts and algorithms at the core of modern machine learning.