Objectives/Vision
Artificial Intelligence (AI) is rapidly pervading our society. Applications of AI include: self-driving cars, personal assistants, surveillance systems, robotic manufacturing, machine translation, financial services, cyber security, web search, video games, image and signal analysis, machine vision, code analysis and product recommendations. Such applications use AI methods and techniques to interpret information from a wide variety of sources and use it to enable intelligent, social, adaptive, goal-directed behavior.
Modern AI often involves adaptive systems that are based on automatic learning from data, and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of smart devices.
AI is a flourishing research field whose recent results are having increasing economical and social impacts on society.
By blending standard classes with recitations and lab sessions, our program ensures that each student masters the theoretical foundations and acquires hands-on experience in each subject. Besides a core of methodological units, the program includes more specialty-oriented electives so that, overall, students can round up their skills on leading edge applications and techniques, including the possibility of implementing them as final projects of the courses.
Career opportunities
A master degree in Artificial Intelligence opens career opportunities in companies that are building the next generation of intelligence and language understanding for their products: for example intelligent personal assistants, opinion mining systems, customer support system, biomedical applications, computer games, smart adaptive devices, robots, smart planning systems. The curriculum provides the skills for working in key positions, in knowledge-intensive companies or research centers. The department has strong connection with many leading companies like Google, Yahoo!, Microsoft, LinkedIn that are investing large resources in AI.
This master course also provides a solid background for a Ph.D. program in Computer Science or an equivalent degree.
Study plan
First year
Semester 1 |
CFU |
Semester 2 |
CFU |
| Artificial intelligence fundamentals | 6 | Human language technologies | 9 |
| Computational mathematics for learning and data analysis | 9 | Parallel and distributed systems: paradigms and models | 9 |
| Machine learning | 9 | Intelligent Systems for pattern recognition | 9 |
| Group: AI elective 6 CFU | 6 | Group: AI elective 6 CFU | 6 |
| 30 | 33 |
Second year
Semester 3 |
CFU |
Semester 4 |
CFU |
| Smart applications | 9 | Thesis | 24 |
| Group: AI elective 9 CFU | 9 | Group: AI elective 6 CFU | 6 |
| Group: free choice | 9 | ||
| 27 | 30 |
Group: AI electives (9 CFU)
Algorithm engineering (BD)
Data mining (BD)
Mobile and cyber-physical systems (ICT)
Group: AI electives (6 CFU)
Information retrieval (BD)
Computational neuroscience (ING)
Semantic web
Robotics
Social and ethical issues in computer technology
Computational models for complex systems
3D Geometric Modeling & Processing
Algorithmic Game Theory
Laboratory on ICT Startup Building
Introduction to Quantum Computing
Computational Health Laboratory
Continual learning
For more details on course contents: