|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
The goal of this course is to provide an overview of classical Machine Learning algorithms, discussing modeling and computational aspects.
Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.
Students will be provided with basic ideas behind statistical learning and a number of prototypical supervised approaches, including, local methods, regularization networks, linear and non linear models. The Course also covers basic unsupervised problems such as clustering and dimensionality reduction. Special effort is devoted to discussing how to set up a reliable machine learning pipeline.
Students will be involved in project activities.
Basic probability, calculus, linear algebra, programming.
Classes and practical lab sessions.
Material provided by the instructors (slides and papers), see the course Aulaweb page additional references.
NICOLETTA NOCETI (President)
LORENZO ROSASCO (President Substitute)
ALESSANDRO VERRI (Substitute)
All class schedules are posted on the EasyAcademy portal.