Machine Learning
      
  
                  A.Y. 2020/2021
      
      
  
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).
      
      
  
  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).
      
      
  
  Lesson period: First 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
Responsible
              
          Lesson period
              First semester
          
FIS/03 - PHYSICS OF MATTER 
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
Lessons: 42 hours
Professor:
Barbieri Carlo
Professor(s)
    
            Reception:
Tue 14:00-15:00 (during the semester), or email me anytime for an appointment
My office is on floor 1 of LITA building, Phys. Dept., Via Celoria 16