Machine Learning and Statistical Learning
A.Y. 2026/2027
Learning objectives
The course introduces students to the most important algorithmical and statistical machine learning tools. The first part of the course focuses on the statistical foundations and on the methodological aspects. The second part is more hands-on, with laboratories to help students develop their software skills.
Expected learning outcomes
Upon completion of the course students will be able to:
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
4. provide statistical interpretations of the results.
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
4. provide statistical interpretations of the results.
Lesson period: Second four month period
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
Second four month period
Modules or teaching units
Machine Learning
INFO-01/A - Informatics - University credits: 6
Lessons: 40 hours
Statistical Learning
STAT-01/A - Statistics - University credits: 6
Lessons: 40 hours
Professor:
Salini Silvia
Professor(s)
Reception:
The student reception on Thesday from 10.00 to 13.00 in presence of via Teams (is better to agree an appointment) - Next Tuesday's student reception will not be held due to other academic commitments. Please contact the professor for another appointment.
DEMM, room 30, 3° floor or in Teams