Data and Natural Language Technologies
A.Y. 2025/2026
Learning objectives
1. Provide students with in-depth knowledge of data and NLP technologies.
2. Develop advanced skills in the use of tools and frameworks for data analysis
3. Deepen students' understanding of natural language processing principles and its applications.
4. Train students in designing and implementing machine learning-based solutions.
5. Enable students to design and implement study and research activities based on data and language analysis methods.
2. Develop advanced skills in the use of tools and frameworks for data analysis
3. Deepen students' understanding of natural language processing principles and its applications.
4. Train students in designing and implementing machine learning-based solutions.
5. 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
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)