Machine Learning: Theory and Algorithms



Giovanni Neglia


  • 2 ECTS

The course introduces the mathematical foundations of machine learning.
Its first goal is to formalize the main questions behind machine learning: What is learning? How can a machine learn? Is learning always possible? How do we quantify the resources needed to learn? To this purpose, the course presents the probably-approximately correct (PAC) learning paradigm. Its second goal is to present several key machine learning algorithms and show how they follow from general machine learning principles.
The course has a theoretical focus, and the student is assumed to be comfortable with basic notions of probability, linear algebra, analysis, and algorithms.