Machine Learning
A.Y. 2026/2027
Learning objectives
The goal of the course is to discuss automatic methods to make predictions and build models starting from available data. The course
will teach the student the theoretical bases of machine learning (fundamentals of statistical learning theory, classification, regression)
and common methods for typical tasks (e.g., clustering and dimensional reduction).
will teach the student the theoretical bases of machine learning (fundamentals of statistical learning theory, classification, regression)
and common methods for typical tasks (e.g., clustering and dimensional reduction).
Expected learning outcomes
The student will be able to analyse data choosing the most appropriate method among well-established ones. Moreover, they will
be familiar with the notions and the language which is common to the disciplines that employ such methods (e.g., computer science,
economy, mathematics).
be familiar with the notions and the language which is common to the disciplines that employ such methods (e.g., computer science,
economy, mathematics).
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
PHYS-02/A - Theoretical Physics of Fundamental Interactions, Models, Mathematical Methods and Applications - University credits: 3
PHYS-04/A - Theoretical Physics of Matter, Models, Mathematical Methods and Applications - University credits: 3
PHYS-04/A - Theoretical Physics of Matter, Models, Mathematical Methods and Applications - University credits: 3
Lessons: 42 hours
Professor:
Gherardi Marco
Professor(s)