Entry points, 1st year, Common Courses
Academic year 2025/2026
General information
- Eötvös Loránd University, Hungary
- Name of the academic coordinator: László Gulyás, lgulyas@inf.elte.hu
- Polytechnic University of Milan, Italy
- Name of the academic coordinator: prof. Davide Martinenghi / Programme manager, federico.schiepatti@polimi.it
- Université Côte d’Azur, France
- Name of the academic coordinator: Jean Martinet, jean.martinet@univ-cotedazur.fr / Françoise Baude, francoise.baude@univ-cotedazur.fr
- University of Trento, Italy
- Name of the academic coordinator: Marco Roveri - marco.roveri@unitn.it
- Universidad Politécnica de Madrid, Spain
- Name of the academic coordinator: Marta Patino: mpatino@fi.upm.es
- University of Rennes, France
- Name of the academic coordinator: Alvaro Pina Stranger: alvaro.pinastranger@univ-rennes.fr
- University of Turku, Finland
- Name of the academic coordinator: Tapio Pahikkala: aatapa@utu.fi
Entry universities offer a modular curriculum consisting of (1) a core module on Artificial Intelligence and Computer Science and (2) a core module on Emotion Artificial Intelligence.
The aim of Module 1 is to provide students with a comprehensive theoretical and practical knowledge of mathematics and computer science, machine learning, deep learning, data representation and processing, as well as ethical, privacy and security considerations.
To fully apply the acquired competencies, the aim is also to enable the student to make purpose-oriented use of the latest deep learning frameworks and open-source neural network software functionalities.
Module 2 allows students to acquire a comprehensive knowledge and carry out relevant activities in the field of human cognition, which are deepened in selected chapters, such as understanding human cognitive and perceptual functions in order to lay the foundation for further analysis of human intentions, as well as cognitive control, social behaviour, language, memory, emotions and general perceptions.
The practical applications of Module 2 are covering the recognition, correct interpretation, expression and matching the intentions of the human partner and the embodied artificial intelligence. Different modalities as emotion-sensitive data sources and natural language processing are also incorporated in the curriculum.
Eötvös Loránd University (ELTE)
Compulsory Major Courses | Semester | ECTS |
Methods and Tools for Artificial Intelligence Applications | 1 | 6 |
Introduction to Data Science | 1 | 6 |
Deep Network Developments | 1 | 6 |
Advanced Deep Network Development | 1 | 6 |
Legal and ethical aspects of DS and AI | 1 | 4 |
Affective Computing | 2 | 6 |
Elective Major Courses | ||
Cognitive Science | 2 | 6 |
Embodied Intelligence | 2 | 6 |
Introduction to NLP | 2 | 6 |
Compulsory I&E Courses |
| |
Innovation and Entrepreneurship Basics | 1 | 6 |
Business Development Lab I. | 1 | 4 |
Innosocial Aspects of the Entrepreneurship | 2 | 6 |
Business Development Lab II. | 2 | 4 |
Elective I&E Courses | ||
I&E for Venture Creation | 2 | 4 |
Polytechnic University of Milan (POLIMI)
Compulsory Major Courses | Semester | ECTS |
SOFTWARE ENGINEERING 2
| 1 | 5 |
SYSTEMS AND METHODS FOR BIG AND UNSTRUCTURED DATA | 1 | 5 |
FOUNDATIONS OF ARTIFICIAL INTELLIGENCE | 1 | 5 |
COMPUTING INFRASTRUCTURES | 2 | 5 |
MACHINE LEARNING | 2 | 6 |
Elective Major Courses | ||
ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING | 1 | 5 |
RECOMMENDER SYSTEMS | 1 | 5 |
UNCERTAINTY IN ARTIFICIAL INTELLIGENCE | 1 | 5 |
DATA MINING | 1 | 5 |
IMAGE ANALYSIS AND COMPUTER VISION | 1 | 5 |
ADVANCED USER INTERFACES | 1 | 5 |
NATURAL LANGUAGE PROCESSING | 2 | 5 |
COMPUTER ETHICS | 2 | 5 |
ROBOTICS AND DESIGN | 2 | 5 |
I&E Courses |
| |
DESIGN THINKING FOR BUSINESS | 1 | 5 |
HIGH-TECH ENTREPRENEURSHIP | 2 | 5 |
DIGITAL BUSINESS LAB | 2 | 10 |
COMMUNICATION AND ARGUMENTATION | 2 | 5 |
PHILOSOPHICAL ISSUES OF COMPUTER SCIENCE | 2 | 5 |
AGILE INNOVATION | 2 | 5 |
MULTIDISCIPLINARY PROJECT (I&E SUMMER SCHOOL) | - | 4 |
Université Côte d’Azur (UCA)
Compulsory Major Courses | Semester | ECTS |
Data science | 1 | 3 |
Deep Learning | 1 | 3 |
Mathematics and Statistics | 1 | 3 |
Modeling and Optimalization in ML | 1 | 3 |
Ethical aspects of data | 2 | 3 |
Human/Robot interaction | 2 | 3 |
Natural Language Processing | 2 | 3 |
Elective Major Courses | ||
Individual R&D project | 1 or 2 | 3 |
Introduction to Constraint Programming | 1 | 3 |
Problem Solving | 1 | 3 |
Information visualization | 1 | 3 |
Blockchain & Privacy | 1 | 3 |
Introduction to Security | 1 | 3 |
Security & Privacy 3.0 | 1 | 3 |
Computer networks | 1 | 3 |
Large Scale distributed systems | 1 | 3 |
Big data technologies | 1 | 3 |
Data valorisation | 2 | 3 |
Advanced Logic | 2 | 3 |
Anthropology and Ethics of Technics | 2 | 3 |
Virtual Reality | 2 | 3 |
Operational Research | 2 | 3 |
Graphs | 2 | 3 |
Functional programming | 2 | 3 |
Advanced Computer networks | 2 | 3 |
Creating interactive virtual worlds | 2 | 3 |
Web | 2 | 3 |
Artificial intelligence engineering | 2 | 3 |
Embedded Artificial Intelligence, sensors, actutators | 2 | 3 |
Data Valorisation | 2 | 3 |
Reinforcement learning | 2 | 3 |
Parallelism | 2 | 3 |
Compulsory I&E Courses |
| |
Basics in I&E (technology ideation and basics) | 1 | 3 |
Business Dev Lab 1 (and project management) | 1 | 3 |
Business Intelligence 1 (data science for business) | 1 | 3 |
I&E for venture creation (summer school format) | 2 | 4 |
Business Dev Lab (with application of business concepts learned earlier) | 2 | 5 |
Elective I&E Courses | ||
I&E complementary course 1 (strategy and internationalisation) | 2 | 3 |
Business Intelligence 2 (marketing) | 2 | 3 |
University of Trento (UNITN)
Compulsory Major Courses | Semester | ECTS |
AI and Ethics | 1 | 6 |
Machine Learning/Deep learning | 1 and 2 | 12 |
Signal Image and Video | 1 | 6 |
Natural Language Understanding | 2 | 6 |
Compulsory I&E Courses |
| |
AI and innovations | 2 | 6 |
Business Development lab | 2 | 9 |
ICT Innovation | 2 | 9 |
Business Dev Lab (with application of business concepts learned earlier) | 2 | 5 |
Universidad Politécnica de Madrid (UPM)
Compulsory Major Courses | Semester | ECTS |
Statistical Data Analysis | x | 3 |
Data Processes | 1 | 4.5 |
Data Visualization | 1 | 3 |
Intelligent Systems | 1 | 4.5 |
Cloud Computing | 1 | 4.5 |
Deep Learning | 2 | 3 |
Information Retrieval | 2 | 4.5 |
Emotion and Sentiment Analysis in text | 2 | 6 |
Image mining | 2 | 3 |
Elective Major Courses | ||
Big Data | 1 | 3 |
Experimentation in software engineering | 2 | 4.5 |
Programming for AI | 2 | 5 |
Compulsory I&E Courses |
| |
Introduction to Innovation and Entrepreneurship Management | 1 | 6 |
Introduction to Technology Watch | 2 | 4.5 |
Business Development Lab | 2 | 6 |
Launching of ICT products | 2 | 2 |
Elective I&E Courses | ||
I&E seminars | 2 | 5 |
University of Rennes (UR)
Compulsory Major Courses | ECTS |
ACO - Object-Oriented Analysis and Design | 5 |
BDD - Advanced Databases | 4 |
RO - Operations Research | 5 |
BDA - Basics of Data Analysis | 6 |
WS - Semantic Web Technologies | 5 |
SBD - Database Security | 5 |
MPC - Machine learning | 5 |
TWA - Technological Watch | 5 |
Compulsory I&E Courses | |
IEB - Innovation & Entrepreneurship (basics) | 5 |
BDL1 - Business Development Lab | 5 |
BDL2 - Business Development Lab | 5 |
Elective I&E Courses | |
KNI - Knowledge and Intangible Asset Management | 5 |
University of Turku (UTU)
Compulsory Major Courses | ECTS |
Statistical Data Analysis | 5 |
Data Analysis and Knowledge Discovery | 5 |
Evaluation of Machine Learning Methods | 5 |
Introduction to Deep Learning | 5 |
Introduction to Human Language Technology | 5 |
Cognitive Neuroscience | 5 |
Compulsory I&E Courses | |
Introduction to Innovation and Business | 5 |
Lean Digital Business Design | 10 |
Summer School | 4 |
Elective I&E Courses | |
Knowledge and Innovation Management | 5 |
Enterprise Architecture | 6 |
Digital Business | 3 |
Digital Business Models | 3 |
Exit points, 2nd year, Specialisation
Academic year 2026/2027
Eötvös Loránd University (ELTE)
Specialization: Humane Aspects of Applied Emotional AI
To explore and understand the broad dimension of new techniques and applications supported by Emotional AI, focusing on human intention and the expression of Emotional through different modalities, together with critical thinking and general ethical considerations.
Compulsory Major Courses | ECTS |
Natural Language Processing | 12 |
Thesis | 30 |
Specialisation Courses/Electives |
|
Computational Intelligence | 5 |
Collective Intelligence | 6 |
3D Point Cloud Processing and Analysis | 6 |
3D Computer Vison | 6 |
I&E Courses |
|
Innovation and Entrepreneurship Study | 6 |
EURECOM
Specialization: Emotional AI embodied in Multimodal Agents
Design, development and deployment of Emotional AI in Multimodal Agents. Multimedia analysis (face recognition, facial beauty assessment, sound processing, sentiment analysis). Neuro-symbolic systems. Conversational assistants with Emotional roles. Bias, fairness and ethical aspects of Emotional AI.
Compulsory Major Courses | ECTS |
Machine Learning and Information System (MALIS) | 5 |
Database Management System Implementation (DBSYS) | 5 |
Semester Project | 8 |
Language Course | 1 |
Thesis/Internship | 30 |
Specialisation Courses |
|
Distributed Systems and Cloud Computing (CLOUDS) | 5 |
Semantic Web and Information Extraction Technologies (WebSem) | 2.5 |
Quantum Information Science (QUANTIS) | 2.5 |
Optimization Theory With Applications (OPTIM) | 2.5 |
Digital Image Processing (ImProc) | 2.5 |
Sound and Music Processing (SoundProc) | 2.5 |
Basics on Reinforcement Learning (ReLearn) | 2.5 |
I&E Courses |
|
Innovation and Entrepreneurship Study | 6 |
Universidad Politécnica de Madrid (UPM)
Specialization: Practical Emotional AI: healthcare
Design and deployment Emotional AI solutions in practice. Usage of cloud resources, integration of different devices and systems with its application to healthcare.
Compulsory Major Courses | ECTS |
Data Analysis | 4.5 |
Image Processing, Analysis and Classification | 5 |
Open Data and Management Graphs | 4.5 |
Massively parallel machine learning | 4.5 |
Thesis/Internship | 30 |
Specialisation Courses/Electives |
|
Complex Data Analysis | 4.5 |
Time Series | 4.5 |
Cloud Computing | 4.5 |
I&E Courses |
|
Innovation and Entrepreneurship Study | 6 |
Université Côte d’Azur (UCA)
Specialization: Holisitic interdisciplinary aspects
To understand the multifaceted domain of Emotionals, from signal processing and machine learning dimensions of Emotional capture, to psychological, behavioral, societal, and ethical aspects.
Compulsory Courses | ECTS |
Decision theory and health care | 2 |
AI and emotions | 2 |
Multimodal emotion recognition from video and biosignals | 2 |
Emotion and decision-making process | 2 |
Behavioural economics and emotions | 2 |
Machine learning for image analysis | 2 |
R&D Project | 6 |
Thesis/Internship | 30 |
Specialisation Courses/Electives |
|
Applied artificial intelligence | 2 |
Artificial Intelligence engineering | 2 |
Advanced topics in deep learning | 2 |
Discourse dialog modeling | 2 |
Spiking neural networks | 2 |
Reinforcement learning | 2 |
Virtual Reality | 2 |
Advanced Optimization | 4 |
Fundamentals of ML | 4 |
I&E Courses |
|
I&E study (in Emotion AI scope) | 6 |
University of Trento (UNITN)
Specialization: Emotional AI in Robotic Applications
Design, development and implementation of Emotionally intelligent AI equipped robotic systems for multifaced domain applications. Acquire expertise in design, development and implementation of advanced human computer interaction going beyond the interaction paradigms traditionally adopted in Human Computer Interaction, interactive multisensory networked systems, collaborative Emotionally intelligent or emphatic robotic systems. Methods and means for developing Emotionally intelligent systems in different contexts.
Compulsory Courses | ECTS |
Affective Computing | 6 |
Advanced HCI | 6 |
Human-Centric AI | 6 |
Thesis/Internship | 30 |
I&E Courses |
|
Innovation and Entrepreneurship basic | 6 |
University of Turku (UTU)
Specialization: Emotional AI in healthcare and medicine
Emotional AI in healthcare and medicine: Design, development, and implementation of Emotionally intelligent AI systems for healthcare applications. Gamification strategies for designing interactive healthcare solutions that enhance patient engagement and Emotional well-being. Methods and means for evaluating the effectiveness of these systems in appropriate contexts.
Compulsory Courses | ECTS |
Programming for Health Wearables | 5 |
Gamification and Serious Games | 5 |
Acquisition and Analysis of Biosignals | 5 |
Thesis/Internship | 30 |
Specialisation Courses |
|
Introduction to Game Development Tools | 5 |
Machine learning health technology project | 5 |
I&E Courses |
|
I&E study | 6 |