EFELIA Côte d'Azur
Structure : EUR SPECTRUM Niveau du cours : M1, M2, Doctorat |
Code de l'UE : RMU02D Semestre : pair |
Lieu d'enseignement : Campus Valrose / Géoazur Langue : anglais |
Public
Students in the first or second year of a Master's program at EUR SPECTRUM, as well as PhD students.
Prerequisites
There are no prerequisites for this course.
Organization
- Remote: 12 hours of lectures (CMs) common to all students
- In-person : 12hrs of labs (TDs) focusing on disciplinary applications, where students will be divided into two groups (chemistry or geoscience) according to their field of specialization
About
- Description
-
Learning outcomes
At the end of this course, you will be able to:
- Explain the fundamental concepts and methods of AI
- Understand the strengths and limitations of modern AI systems
- Begin incorporating AI advancements into the exploration of topics within their respective fields
Modern Artificial Intelligence (AI) represents a series of advancements in computer science, applied mathematics, and statistics. It introduces innovative methods and tools that are increasingly shaping professional practices and influencing society.
This introductory course aims to familiarize students with the fundamental concepts and practical applications of AI, particularly in the fields of chemistry and geoscience. It provides students with the opportunity to understand and engage with these technologies, already embedded in current practices, such as analyzing data from physics experiments, exploring chemical spaces, or predicting earthquakes. The course will place a strong emphasis on machine learning methods, while also addressing the limitations of current techniques and the scientific, societal, and environmental challenges associated with these technologies, to enable students to identify the opportunities that AI brings to their respective disciplines.
Upon successful completion of this course, you will be awarded 3 ECTS.
This course was co-developed by EUR SPECTRUM and EFELIA (École Française de l'Intelligence Artificielle) Côte d’Azur.Professor
- Vanna Lisa COLI (Vannalisa.COLI@univ-cotedazur.fr)
- Teaching methods
- Theoretical instruction
Case studies
Labs: practical applications and exercises to reinforce theoretical concepts - Assessment
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Continuous assessment.
- Evaluation of the CM part in the form of interdisciplinary group work
- Graded TD
- Equipment
- Bringing a personal computer to class is recommended but not required.
- Bibliography
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- Casilli, A. (2019), En attendant les robots. Enquête sur le travail du clic, Paris, Seuil
- Crevier, D. (1999), À la recherche de l’intelligence artificielle, Paris, Flammarion
- Floridi, L. (2023), The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities, Oxford, Oxford University Press
- Leonelli, S. (2016), Data-Centric Biology: A Philosophical Study, Chicago, University of Chicago Press.
- Other ressources
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- University of Helsinki & MinnaLearn (2018). A free online introduction to artificial intelligence for non-experts. https://course.elementsofai.com/
- Andrew Ng (s.d.). AI for Everyone. https://www.deeplearning.ai/courses/ai-for-everyone/
- Daniel Leufer & Alexa Steinbrück (2020), AI Myths. https://www.aimyths.org
- Support
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- écri+ : to improve your written French.
- Centre de ressources en langues : to improve your foreign language skills (French or other).
- METODA : to improve your documentary research skills.
- S'orienter / Se réorienter : to be advised by the university's career counsellors.
- Centre de santé et aide sociale : to look after your physical and mental health, and to seek support in the event of social hardship.
- Cellule Handicap : support for students with disabilities.
- Plateforme de signalement : to report acts of violence, harassment or discrimination (sexual and gender-based violence, LGBTphobia, racism, xenophobia, etc.) you have witnessed or experienced at the university, and to get support.
Programme
Session | Date | Classroom | Professor | Topic |
1 | 27/02/2025 9h00 - 12h00 |
Remote | Vanna Lisa Coli | Introduction to AI:
|
2 | 06/03/2025 9h00 - 12h00 |
Remote | Vanna Lisa Coli | Introduction to Machine Learning : supervised and unsupervised learning (regression, classification, dimension reduction) |
3 | 13/03/2025 3 hours (morning) |
M03 (Campus Valrose) / Géoazur (salle à déterminer) | Spectrum lecturer | Lab (TD): practical applications and exercises |
4 | 20/03/2025 9h00 - 12h00 |
Remote | Vanna Lisa Coli | Introduction to deep learning: Multi-layer neural networks Convolutional neural networks for image classification |
5 | 27/03/2025 3 hours (morning) |
M03 (Campus Valrose) / Géoazur (salle à déterminer) | Spectrum lecturer | Lab (TD): practical applications and exercises |
6 | 03/04/2025 9h00 - 12h00 |
Remote | Vanna Lisa Coli | The challenges of datasets and data bias |
7 | 27/03/2025 3 hours (morning) |
M03 (Campus Valrose) / Géoazur (salle à déterminer) | Spectrum lecturer | Lab (TD): practical applications and exercises |
8 | 27/03/2025 3 hours (morning) |
M03 (Campus Valrose) / Géoazur (salle à déterminer) | Spectrum lecturer | Lab (TD): practical applications and exercises |
Ce travail a bénéficié d'une aide de l'Etat gérée par l'Agence Nationale de la Recherche (ANR) au titre de France 2030 pour le projet EFELIA Côte d’Azur portant la référence ANR-22-CMAS-0004.