Introduction to artificial intelligence for scientists and engineers

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

Teaching methods
Theoretical instruction
Case studies
Labs: practical applications and exercises to reinforce theoretical concepts
Assessment

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
  • 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
Support

Programme

Session Date Classroom Professor Topic
1 27/02/2025
9h00 - 12h00
Remote Vanna Lisa Coli Introduction to AI:
  • Definition and demystification
  • Constructing AI problematics
  • Introduction to symbolic and statistical models
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
Important: This syllabus has no binding value. Its content may change during the course of the year.

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.