Since september 2025, UniCA runs an entry point in A.I with Affective Computing, whose all details are given here.
The exit point described in this page, opens in September 2026, offers a specialization entitled Holistic interdisciplinary aspects .
Be aware of the great opportunity by studying as an ENTRY student at UCA => you will be registered to the UniCA Polytech Nice Sophia engineering degree in Sciences Informatiques (Computer science). This engineering degree is well-recognized through two labels: by the French CTI and the European EUR@ACE, still it requires you to commit to an extra requirement: a French proficiency level at least A2 at the end of (or at worst 2 years after) your studies as an UniCA engineering student.
Sorry, but this opportunity cannot apply to EXIT students, as the number of studying semesters in the premises of UniCA Polytech engineering school is too low (only 1..., should be at least equal to 2 to get opportunity to graduate with a CTI labelled engineering degree) => you can only graduate with the Master degree of UCA.
Check here for more information about studying at Sophia Tech campus of Université Côté d'Azur (UniCA) as international student.
Second Year EIT Digital track in Artificial Intelligence (program 2026-27 in progress)
Semester 3
Mandatory as elective courses are most of the times spread on either of the two periods (quarters of consecutive 8 weeks including last one for the written exam). The semester lasts from early september up to early march. Syllabus of some of the DSC /AI with Affectice Computing entry or exit courses are collected in this zip file. (to be updated for 2026-27). The indicated slot of each course is subject to modification.
Compulsory courses (6 ECTS)
| Period 1 Mandatory courses | Schedule | |
|---|---|---|
| Emotion and A.I. | New course | Coefficient 2 |
| Accessibility of interactive systems | Wed. morning | Coefficient 2 |
| Period 2 Mandatory courses | Schedule | |
|---|---|---|
| Text mining and Natural Language Processing | Monday morning | Coefficient 2 |
Elective courses (12 ECTS): choose sufficient courses to collect 12 ECTS (or more)
| Period 1 Elective courses : mid sept-mid nov | Schedule | |
|---|---|---|
| EIIN947 (Panorama of) Big Data technologies | Monday morning | Coefficient 2 |
| EIENIV9 Information visualisation | Monday morning | Coefficient 2 |
| EIIN932 An algorithmic approach to distributed systems | Monday afternoon | Coefficient 2 |
| EIIN935 Data Science | Monday afternoon | Coefficient 2 |
| EIENRE9 Reinforcement learning (from CS dept) | Monday afternoon | Coefficient 2 |
| EIENAA9 Applied A.I. | Tuesday morning | Coefficient 2 |
| EIExxx Semantic Web and Linked data | Tuesday morning | Coefficient 2 |
| SIIN907 Graph algorithms and combinatorial optim | Tuesday afternoon | Coefficient 2 |
| EIENDE9 Deep learning | Tuesday afternoon | Coefficient 2 |
| EIINxxx Cybersécurité-1 (in French only) | Tuesday afternoon | Coefficient 2 |
| EIENML9 Machine Learning for Image Analysis | Wed morning | Coefficient 2 |
| DS4H ICT & environment | Thursday morning, partly spans also on period 2 | Coefficient 2 |
| DS4H Javascript and web | Thursday late afternoon partly spans also on period 2 | Coefficient 2 |
| DS4H Quantum technologies | Thursday morning (Sciences faculty campus "Valrose") partly spans also on period 2 | Coefficient 2 |
| ELMI (Introduction to) Behavioural economics | xxx (Economy faculty campus "St Jean Angely") partly spans also on period 2 | Coefficient 2 |
| ELMI Digital economics | xxx (Economy faculty campus "St Jean Angely") partly spans also on period 2 | Coefficient 2 |
| Advanced Optimisation | Wednesday afternoon, Sciences faculty campus "Valrose" from 1pm |
Coefficient 2+2 |
| Period 2 Elective courses : Nov-Jan | Schedule | |
|---|---|---|
| Agentic A.I. | New course -TBC- | Coefficient 2 |
| xxx Multimedia emotion recognition from video and biosignals | New course -TBC- | Coefficient 2 |
| Emotion and Decision making process | New Course -TBC- | Coefficient 2 |
| EIIN906B Blockhain and privacy | Monday morning | Coefficient 2 |
| EIENTA9 Advanced topics in deep learning (maths oriented) | Monday afternoon | Coefficient 2 |
| EIIN952 Advanced deep learning (CS oriented) | Monday afternoon | Coefficient 2 |
| SIINC906 Introduction to Security | Monday afternoon | Coefficient 2 |
| EIIN908B Machine learning for networks | Tuesday morning | Coefficient 2 |
| EIENES9 Evaluation des systèmes interactifs (can be taught in French) | Tuesday morning | Coefficient 2 |
| EIIN938 AI engineering | Tuesday afternoon | Coefficient 2 |
| EIERO921 Reinforcement learning (from Robotics dept) | Tuesday afternoon | Coefficient 2 |
| EIENxxx Knowledge graph-based A.I. | Tuesday afternoon | Coefficient 2 |
| EIEMA903 Frugal computer vision | Tuesday afternoon | Coefficient 2 |
| EIIN943 Large scale distributed systems | Tuesday afternoon | Coefficient 2 |
| EIENVR9 Virtual reality | Wed. morning | Coefficient 2 |
| SIIN931 Virtualized infrastructures for cloud computing | Friday morning | Coefficient 2 |
Project Fin d'Etudes in Data science (6 ECTS)
| Project Fin d'Etudes | Schedule | |
|---|---|---|
| Personal research and/or development project in Data Science, individual or in small teams (up to 4 people) | Starts september till early march, 4 weeks, almost full time in feb | 6 ECTS |
Innovation and Entrepreunership module (6 ECTS)
Besides, the student must develop a mandatory Innovation and Entrepreneurship (I&E) work of 6 ECTS, as mandated by EIT Digital I&E common specification of masters. This work is coached by the UniCA coordinator in I&E and spans the whole October-Early March period once per two weeks approximatively starting early october. The goal is to reuse on-line I&E material from the Moodle, common to all EIT Digital masters, and apply this to selected business cases. These business cases are most of the time proposed by the various EIT Digital Action Lines partners and the local ecosystem.
Semester 4
Internship/Master thesis (30 ECTS, 4 to 6 months max)
This internship can be done either in our partner research institutions teams at I3S, LJAD, INRIA even if for EIT Digital students, industrial internship is the preferred choice. We provide support and guidance to this aim. Including for outside the Nice - Sophia-Antipolis Technology park. The evaluation of the internship work encompasses three aspects: work achieved as measured by the internship supervisor, written thesis submitted at the end of August and evaluated by the university supervisor, oral defense organized early September (can happen in visio conference mode) and evaluated by a jury of professors. Positions as employee in a company can also be turned as the mandatory period for preparing the master thesis, as soon as the content is approved by the track local coordinator.
Project subjects and internships of former students
This is one way to let known who are the students that have studied one year in A.I. with Affective Computing at UniCA. This includes the name of their exit point, or respectively entry point. And what are the topics that they have been able to be in touch with. For Master 2 students, is also indicated the title of their master thesis, where they prepared it, and if available, where they are now employed.
Master 1, Cohort 2025-26
| Project or Internship Topic/Title | Team | Student(s) |
|---|---|---|
| xxx | Maasai/Sparks team, CNRS UCA I3S/INRIA | Andrea BINETRUY BILON (UTU) |
| xxx | yyy | Cristian FURDUI (ELTE) |