Applied Machine Learning

Smart Infrastructures Developer

Applied Machine Learning

Transportation Engineering and Mobility

Transportation Engineering and Mobility

Applied Machine Learning

A minor study program for Applied Machine Learning can be a valuable addition to your educational journey, especially if you're interested in the fascinating field of algorithms and statistical models that enable computers to learn from data and make predictions or decisions. The minor also exploits some of the competencies already acquired in the major educational roadmap. It lets the students view some studied subjects from a new perspective. Applied Machine Learning is required to apply effective and modern techniques in the smart mobility arena. A deep understanding of the principles for integrating technologies into infrastructures and enhancing the efficiency, sustainability, and functionality of sensors and sensing systems. Data learning techniques allow the extraction of valuable insights from the data collected, examining innovative transportation systems, including intelligent traffic management, autonomous vehicles, and public transportation innovations.

The minor program for Applied Machine Learning will equip you with a well-rounded understanding of Smart Mobility development, including the technologies, practices, and principles involved in creating more efficient, sustainable, and user-centric infrastructures for the future. Be sure to check with your educational institution for specific course offerings and requirements for this minor program.

The Minor in Applied Machine Learning provides an educational path that includes activities up to 30 credits. The student selects modules from a portfolio dedicated to acquiring methodologies to propose and support the development of smart mobility solutions. The selection of the modules is submitted by the student and approved by a Steering Committee. The Study Program in Transportation Engineering and Mobility has framed a roadmap that automatically matches all the requirements for Applied Machine Learning.
The roadmap ensures the acquisition of a Digital Badge.

Entry key and information

Applied Machine Learning Fact Sheet

Awards Level 2
Mode of Study Full Time
Duration 2 years + (activities for extra 10 ETCS are requested)
Location 21, via Claudio - 80125 Napoli (Italy) -

Entry Requirements:

Candidate students require a level 6 qualification (or above), according to the European Qualifications Framework (EQF). Moreover, they should provide evidence of English language proficiency at level B2 or above (otherwise, individual English proficiency testing could be done immediately after enrolment). For non-EU candidate students, a B2 English certificate is strictly needed. A certificate issued by the bachelor's degree University or an MOI (Medium of Instruction) certificate is also accepted.

The MSc in Transportation Engineering And Mobility preferably requires a bachelor's in Engineering. Otherwise, particular conditions must be checked:

  • At least 36 ETCS in basic sciences (maths, physics, etc.)
  • At least 39 ETCS in industrial engineering, information, and communication technology, or civil engineering; of these, at least 18 ETCS in civil engineering.

Non-EU candidates must follow a pre-admission roadmap for obtaining a visa to study in Italy.

For more admission information, see the relevant web page.

Additional activities

It is requested an additional amount of activities (10 extra ECTS):

  • 4 ETCS in additional seminars, workshops, and lab activities
  • 6 ETCS for a subject on smart infrastructures, based on lectures and final examination.