Data Natural Language and Technologies
A.Y. 2025/2026
Learning objectives
- Provide students with in-depth knowledge of data and NLP technologies.
- Develop advanced skills in the use of tools and frameworks for data analysis
- Deepen students' understanding of natural language processing principles and its applications.
- Train students in designing and implementing machine learning-based solutions.
- Enable students to design and implement study and research activities based on data and language analysis methods.
- Develop advanced skills in the use of tools and frameworks for data analysis
- Deepen students' understanding of natural language processing principles and its applications.
- Train students in designing and implementing machine learning-based solutions.
- Enable students to design and implement study and research activities based on data and language analysis methods.
Expected learning outcomes
Upon completion of the course, students should be able to
- Apply advanced concepts of data technologies in practical contexts;
- Use NLP models to analyze natural language and solve specific problems;
- Apply advanced machine learning techniques in various application contexts;
- Successfully complete complex application projects that integrate data and NLP technologies.
- Apply advanced concepts of data technologies in practical contexts;
- Use NLP models to analyze natural language and solve specific problems;
- Apply advanced machine learning techniques in various application contexts;
- Successfully complete complex application projects that integrate data and NLP technologies.
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
Responsible
Lesson period
Second semester
Professor(s)
Reception:
On appointment. The meeting will be online by first contacting the professor by email.
Online. In case of a meeting in person, Department of Computer Science, via Celoria 18 Milano, Room 7012 (7 floor)