Coordinated by: Anaïs Ollagnier, Assistant Professor, Université Côte d’Azur, Inria, CNRS, i3S
SCHEDULEFORMAT
Classroom
LOCATION
Campus SophiaTech, Templiers
Prerequisites
- Scientific Bachelor
- Python programming
- Prerequisites and main concepts of Minor AI Introduction
CAPACITY
24 students
ABOUT THIS MINOR
This minor is also open to students from the SPECTRUM graduate school.
- Summary
-
This foundational course on deep learning introduces the core concepts, techniques, and applications of this rapidly evolving field. It will equip you with the knowledge to understand the potential, challenges, and implications of deep learning and prepare you to contribute to the development of advanced AI technologies.
LEARNING OUTCOMES
- Know the principles of Deep Learning (neural network)
- Know how to build models based on neural networks to process structured data, images or text
Throughout the course, you will build and train neural network architectures, including convolutional and recurrent neural networks, and learn to optimize them using strategies like Dropout, BatchNorm, and advanced initialization techniques. By combining theoretical knowledge with hands-on implementation in Python and TensorFlow, you will tackle real-world problems such as object recognition and natural language processing.
Find out more about this minor on Moodle...(course's name "AI : INTRODUCTION TO DEEP LEARNING (DATA ANALYSIS AND DEEP LEARNING)"). - Lecturer
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- Anaïs Ollagnier, Assistant Professor, Université Côte d’Azur, Inria, CNRS, i3S
- Prerequisites
- 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/
- Bibliography
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- 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)
- 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
- Evaluation (updated Feb 26)
-
- Test at beginning of the
5th4th class -27/02/202520/03/2025 - 30 min - SophiaTech Templiers room B214 - 25% of the final grade - Test at beginning of the
8th7th class -17/04/202510/04/2025 - 30 min - SophiaTech Templiers room B214 - 25% of the final grade Student Project - Submission deadline: 17/04/2025 + 15 min presentation - SophiaTech Templiers room B2142 Labs - 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).
- Test at beginning of the
SCHEDULE Spring 2025 (updated Feb 26)
Mind the evaluation modalities and deadlines in the "Evaluation" tab above.
| Date | Time | Course's title | Place |
| 27/02/2025 | 9h00-10h30 | CM : Introduction to Deep Learning | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 6/03/2025 | 8h30-10h00 | CM : Neural Networks and Multilayer Perceptrons (MLP) | Campus SophiaTech, Templiers room B214 |
| 10h00-12h30 | TD | ||
| 13/03/2025 | 9h00-10h30 | CM :Convolutional Neural Networks (CNNs) | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 20/03/2025 | 9h00-10h30 | CM : Encoder/Decoder Networks | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 27/03/2025 | 9h00-10h30 | CM :Data, Embedding, and Latent Spaces | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 3/04/2025 | 9h00-10h30 | CM : Recurrent Neural Networks (RNNs) and Transformers | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 10/04/2025 | 9h00-10h30 | CM : Large Language Models (LLMs) | Campus SophiaTech, Templiers room B214 |
| 10h30-12h30 | TD | ||
| 24/04/2025 | 9h00-10h30 | GANs | Campus SophiaTech, Templiers room B215 |
| 10h30-12h30 | TD |