Coordinated by: Michel Riveill, PR Université Côte d'Azur, Polytech Nice Sophia, I3S
SCHEDULEFORMAT
Classroom
LOCATION
Campus SophiaTech, Lucioles
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 and LIFE graduate schools.
- Summary
-
Find out more about this minor on Moodle...(course's name "ADVANCED AI: ADVANCED MACHINE LEARNING AND DEEP LEARNING")
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 time series
- Lecturers
-
- Antoine Collin (PhD 3IA student for TD supervision)
- Ashwin Moongathottathil-james (ATER Université Côte d'Azur)
- Michel Riveill (PR Université Côte d'Azur, Polytech, 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
-
- Statistics and Machine Learning in Python. Edouard Duchesnay, Tommy Löfstedt (presents Python language for machine learning)
- Python Machine Learning. Sebastian Raschka
- 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)
- Evaluation
-
- Exam during lectures - 50% of the final grade - Date to be confirmed
- Lab presentation - 50% of the final grade - 14/12/2023
SCHEDULE FALL 2023 (updated Oct 13)
Mind the evaluation modalities and deadlines in the "Evaluation" tab above.
| Date | Time | Course's title | Lecturers | Place |
| 12/10/2023 | 9h00-12h30 | Deep learning - General principles | Michel Riveill Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 19/10/2023 | 9h00-12h30 | Multi-layers perceptron | Michel Riveill Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 26/10/2023 | 9h00-12h30 | Auto-Encoder and Latent Representation | Michel Riveill Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 2/11/2023 | No lecture | Break | - | - |
| 9/11/2023 | 9h00-12h30 | Recurrent Neural Network for time series | Michel Riveill Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 16/11/2023 | 9h00-12h30 | Convolutional Neural Network 1 | Ashwin Moongathottathil Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 23/11/2023 | No lecture | SophIA Summit | - | - |
| 30/11/2023 | 9h00-12h30 | Convolutional Neural Network 2 | Ashwin Moongathottathil Antoine Collin |
Campus SophiaTech,Lucioles, room 352 |
| 07/12/2023 | 9h00-12h30 | Object Detection | Ashwin Moongathottathil Antoine Collin |
Campus SophiaTech, Lucioles, room 352 |
| 14/12/2023 | 9h00-12h30 | Recomender Systems and Lab Presentation | Michel Riveill Antoine Collin |
Campus SophiaTech,Lucioles, room 352 |