Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. Machine Learning engines enable intelligent technologies such as Siri, Kinect or Google self driving car, to name a few. At the same time, Machine Learning methods help deciphering the information in our DNA and make sense of the flood of information gathered on the web, forming the basis of a new “Science of Data”. This course provides an introduction to the fundamental methods at the core of modern Machine Learning. It covers theoretical foundations as well as essential algorithms. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.

This introductory course is suitable for undergraduate/graduate students, as well as professionals.

Course Structure:

• 26/06 Monday 9:30-13:00 Lessons - 14:00-16:00 Laboratory
• 27/06 Tuesday 9:30-13:00 Lessons - 14:00-16:00 Laboratory
• 28/06 Wednsday 9:30-12:30 Talks
• 29/06 Thursday 9:30-13:00 Lessons - 14:00-16:00 Laboratory
• 30/06 Friday 9:30-13:00 Lessons

The course started in 2013 has seen an increasing national and international attendance over the years with a peak of over 100 participants in 2015.

NOTE: the course has no registration fee, but participants need to take care of their travel and accommodation needs -- see below for a list of hotels.

Notification of acceptance: To be announced.

Related courses:

• RegML 2015 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.
• ISML2 - Machine Learning 2015. 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.