Minor Artificial Intelligence: Introduction to Deep Learning

• Scientific Bachelor
• Basic Python programming
• The main scientific libraries: NumPy, Pandas, and Matplotlib
• Familiarity with machine learning concepts (e.g., supervised learning, classification, regression).
• An introductory understanding of probability, statistics, and linear algebra is helpful but not mandatory.

If you do not practice Python on a daily basis:

• Autoevaluate yourself to make sure you do have the prerequisites: http://www.i3s.unice.fr/~riveill/python/auto_eval.html
• Review the tutorial: https://www.programiz.com/python-programming/tutorial especially the chapters :
  • Python Introduction
  • Python Flow Control
  • Python Functions
  • Python Datatypes
  • Python Files
• Train yourself:
  • Learn the Basics, Data Science Tutorials, Advanced Tutorials from the tutorial : https://www.learnpython.org/
  • Python Data Science Handbook, Jake VanderPlas, 2017, O’REILLY (Part 1 to 4) – free pdf available on internet
  • Possible to make lab with Google Colab (no installation needed)

• Beginners may consult:
  • Hal Daumé’s A Course in Machine Learning (Chapters 1–5 recommended)
  • The EFELIA Côte d'azur Handbook on AI
• Statistics and Machine Learning in Python. Edouard Duchesnay, Tommy Löfstedt ​(presents Python language for machine learning​)
• Python Machine Learning. Sebastian Raschka​ https://github.com/rasbt/python-machine-learning-book-3rd-edition)
• Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent. A. Géron. O’Reilly​ (practical) - Part II
• Data science : fondamentaux et études de cas: Machine learning avec Python et R. M. Lutz, E. Biernat. Editions Eyrolles​ (In French, introduction level but requires some maths background: https://elmoukrie.com/wp-content/uploads/2022/05/eric-biernat-michel-lutz-yann-lecun-data-science-_-fondamentaux-et-etudes-de-cas-_-machine-learning-avec-python-et-r-eyrolles-2015.pdf)
• FIDLE: Introduction to Deep Learning: https://fidle.cnrs.fr/w3 

Université Côte d'Azur's Library Resources

• Test at beginning of the 4th class - 5/11/2026 - 30 min - SophiaTech Templiers room B215 - 25% of the final grade
• Test at beginning of the 8th class - 03/12/2026 - 30 min - SophiaTech Templiers room B215 - 25% of the final grade
• Two of the practical lab sessions completed during the course will be assessed. Students will not know in advance which two lab sessions will be assessed - 50% of the final grade
• Attendance: Attendance is mandatory for the entire range of transversal courses offered by EUR DS4H (minors and projects). A penalty applies for any unjustified absence in minors: 1 point will be deducted from the final grade for each unjustified absence, up to a maximum of 3 points on the final grade of the course unit (UE).