Objectives/Vision
Artificial Intelligence (AI) is rapidly transforming our society through applications such as self-driving cars, personal assistants, surveillance systems, robotic manufacturing, machine translation, financial services, cybersecurity, web search, video games, image and signal analysis, machine vision, code analysis, and product recommendation systems. These systems employ AI techniques to interpret diverse types of data, enabling intelligent, adaptive, goal-oriented behavior.
This track covers modern AI methodologies, including self-learning systems trained on massive datasets and interacting intelligent agents that perform distributed reasoning and computation. Students explore the integration of sensors, algorithms, and human-computer interfaces, extending into large networks of smart devices.
Career opportunities
The track offers a blend of theoretical instruction and practical labs, ensuring that each student masters the theoretical foundations and acquires hands-on experience. Core courses cover areas such as artificial intelligence fundamentals, machine learning, human language technologies, and intelligent systems for pattern recognition. Elective options have been expanded to allow personalized exploration of advanced AI areas, including continual learning, quantum computing, scalable systems, and algorithmic design.
By engaging with both foundational and cutting-edge topics, students are prepared to contribute to the evolving landscape of AI, addressing complex challenges and driving innovation across various domains.
Graduates are well prepared for careers in top-tier tech companies (both national and international) or to pursue Ph.D. programs in Computer Science or related fields.
Study plan
The track has been recently updated to reflect the evolution of the field. It now includes:
- Generative and Deep Learning, formerly Intelligent Systems for Pattern Recognition, to better represent its content and disciplinary position;
- Human Language Technologies, moved to the second year to build on students’ foundational knowledge;
- Computer Vision, newly added to cover a critical area currently underrepresented;
- Learning on Graph, a 6 CFU elective aligned with current departmental research strengths;
The course Smart Applications has been transformed into an elective to offer greater flexibility.
First year
Semester 1 |
CFU |
Semester 2 |
CFU |
| Artificial intelligence fundamentals | 6 | Generative and deep learning | 9 |
| Computational mathematics for learning and data analysis | 9 | Parallel and distributed systems: paradigms and models | 9 |
| Machine learning | 9 | Group: AI elective 9 CFU | 9 |
| Group: AI elective 6 CFU | 6 | ||
| 24 | 33 |
Second year
Semester 3 |
CFU |
Semester 4 |
CFU |
| Human language technologies (not offered in the a.y. 25/26) | 9 | Group: AI elective 6 CFU | 6 |
| Computer vision (not offered in the a.y. 25/26) | 9 | ||
| Group: AI elective 9 CFU | 9 | ||
| Group: free choice | 9 | Thesis | 24 |
| 36 | 30 |
Group: AI electives (9 CFU)
Algorithm engineering (Sem. 1 )
Algorithm design (Sem. 2) (*)
Data mining (Sem. 1)
Digital Health lab (Sem. 2) (*)
Mobile and cyber-physical systems (Sem. 2)
Group: AI electives (6 CFU)
3D Geometric Modeling & Processing (Sem. 1)
Accelerated Computing (Sem. 1)
Algorithmic Game Theory (Sem. 2)
Competitive programming and contest (Sem. 1) (*)
Computational models for complex systems (Sem. 2)
Computational neuroscience (Sem. 2)
Continual learning (Sem. 2)
Information retrieval (Sem. 1)
Introduction to Quantum Computing (Sem. 2)
Laboratory on ICT Startup Building (Sem. 1)
Learning on Graphs (Sem. 1) (not offered in the a.y. 25/26) (*)
Robotics (Sem. 2)
Scalable Distributed Computing (Sem. 1) (*)
Scientific and large data visualization (Sem. 2)
Semantic web (Sem. 1)
Smart applications (Sem. 1) (not offered in a.y. 25/26 – Smart Applications, 9 cfu, is offered instead)
Social and ethical issues in computer technology (Sem. 2)
(*) Courses offered only to new enrolled students.
Students enrolled before the academic year 2025/2026 can refer to the previous study plan (see the following linked document)