Seminars from the Industry

1st year
Programme main editor:
Onsite in:
ECTS range:



Minimum background on AI and networks.

Pedagogical objectives:

The aim of the course is to introduce the students to the current technological advances and challenges faced by relevant companies (all related to AI, communications, IoT, connected industries, etc.). Through the proposal of challenges, the students will have the opportunity to demonstrate their creativity and to apply the knowledge acquired in the M1 courses.

Evaluation modalities:

Attendance and visualization of the talks, progress reporting, and final presentation.


The course consists of having many invited speakers from companies to expose research, development, and innovation activities related to artificial intelligence for connected systems topics. Each speaker will present the company, the technologies they use/develop, examples of past and ongoing projects, and finally, a few examples of real-world challenges the company is interested in. There will be coordination between the company and the masters professors to check that the challenges are in line with the contents of the M1 courses. After all talks have ended, the students will team up (i.e. groups of 2 or 3) to choose a challenge and make a proposal on how to solve it using their creativity and the knowledge acquired during the M1 courses.

The teams will be supervised by one professor (locally or remotely), and will work on the challenge during part of the semester, with a weekly meeting with the supervisor to show their progress.

In the last part of the course, the students will make a presentation of their proposal to the company (represented by the same speaker, preferably) and a supervisor from the local faculty. Each presentation will be allowed up to 15 minutes, followed by questions from the company representative and the supervisor.

A rubric needs to be defined to make the assessment of the presentations and the weekly meetings.

Required teaching material

● Online learning platform for recording and visualization of the lessons ● Personal computer.

Teaching volume:
12 hours
Supervised lab:
18 hours


  • Laboratory-Based Course Structure
  • Open-Source Software Requirements