Prerequisities

General prerequisites

  • Perspective students that can apply:
    • Students that have graduated, or are graduating, in an  BA/MA (Italian: laurea specialistica) in Engineering, Computer science, Neuroscience, Psychology, Biology, Medicine, Social Sciences, Philosophy with a keen interest in topics of the School, willing to join the network and to working with on interesting advanced problems.
    • Researchers and professionals that aim to an intense upskilling in the field of AI to keep up with such new technologies or to steer their career
  • Because of the multidisciplinary nature of the Curriculum, and the pervasiveness of the School related new technologies, applications are welcome from any area of knowledge, including both scientific and humanity fields.
  • The application is open to students who have terminated all university exams, or close to terminate them, especially if they intend to use the School project for their thesis graduation thesis (Italian: tesi di laurea specialistica).  
  • Each application will be considered on the basis of the student’s specific CV, interests, sought objectives, and on the possibilities of the School.
  • All the materials of the School (video lectures, papers, etc.) are in English.
  • The lessons are in Italian.
  • Students must bring their own devices (laptop, tablet).  
  • Furthermore, the enrollment in the School includes the science2mind membership, as we desire students to become active members of the school network.
  • Students who successfully attend the School are encouraged to remain members of the School network, to possibly become mentors for future applicants, and to come back for a two/three hours visit on the next edition of the School.

Specific prerequisites of perspective students, and knowledge acquired within the School

The school is open to students of all university faculties/departments. The philosophy of the School is indeed that variety is an important asset to foster the School interdisciplinary nature and is in line with its long-life learning open perspective. In this respect the School should be intended as a container of resources from which specific resources will be drawn to tailor a specific learning path onto the learning needs and project of the specific student.

For this reason, it is very important that if you are a perspective student you contact us as soon as possible, before the submission, to evaluate together your case and consider possibilities that fit your needs and goals!

Instead, important prerequisites to fully benefit of the School program are a keen motivation, interest on AI, will to learn, desire to interact with and act within an interdisciplinary network.

We have identified three groups of students coming from different university backgrounds and hence having different knowledge and learning needs as follows.

Students from biology/neuroscience, psychology, philosophy, law, economics, political sciences, communication sciences:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic
  • Knowledge and skills to acquire from the School:
    (2) Capacity to face problems with a quantitative approach and mindset, acquisition of fundamental math and statistics notions needed for machine learning;
    (3) Capacity to program; knowledge and skills on AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to real-life research/application problems

Students from math, physics, chemistry, statistics:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic;
    (2) Capacity to face problems with a quantitative approach and mindset
  • Knowledge and skills to acquire from the School:
    (3) Capacity to program; complements of math and statistics oriented to machine learning; in-depth understanding and skilled-use of AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to real-life research/application problems

Students from engineering, and computer science:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic;
    (2) Capacity to face problems with a quantitative approach and mindset
    (3) Capacity to program; possibly some knowledge on AI
  • Knowledge and skills to acquire from the School:
    (3) Complements of math and statistics oriented to machine learning; in-depth understanding and skilled-use of AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to a real-life research/application problems