Minor Artificial Intelligence: Introduction to Deep Learning

Coordinated by: Michel Riveill, PR Université Côte d'Azur
 

FORMAT

Classroom

LOCATION

Campus SophiaTech, Lucioles

Prerequisites

CAPACITY

24 students

ABOUT THIS MINOR

This  minor is also open to students from the SPECTRUM and LIFE graduate schools.

Summary

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
​​The course on deep learning is fundamental. It will help you understand the capabilities, challenges and implications of deep learning and prepare you to participate in the development of cutting-edge AI technologies. 
​In this course, you will build and train neural network architectures such as convolutional neural networks or recurrent neural networks, and most importantly, you will learn how to improve them with strategies such as Dropout, BatchNorm, different initialization strategies. Theoretical concepts and their industrial applications using Python and TensorFlow will be implemented on object recognition or natural language processing problems.​ 

Find out more about this minor on Moodle...(course's name "ADVANCED AI: ADVANCED MACHINE LEARNING AND DEEP LEARNING")
Lecturers
  • Diane Lingrand (PR Université Côte d'Azur, Polytech Nice Sophia, i3S)
  • Michel Riveill (PR Université Côte d'Azur, Polytech Nice Sophia, i3S)

Prerequisites
If you do not practice Python on a daily basis,
 
Bibliography
Evaluation
  • The course is subject to continuous assessment. It is possible that certain sessions begin with an examination and that the practical work or lab will be assessed.
  • 50% of the mark will be awarded on the basis of a short examination (at least one examination).
  • 50% of the mark will be awarded on the assessment of practical work (at least one assessed practical task). 

SCHEDULE

 
This minor will not open on Fall 2024 semester.
Mind the evaluation modalities and deadlines in the "Evaluation" tab above.
 
Date Time Course's title Lecturers Place
10/10/2024 9h00-12h30 Deep learning - General principles Michel Riveill Campus SophiaTech, Lucioles, room 352
17/10/2024 9h00-12h30 Multi-layers perceptron Michel Riveill Campus SophiaTech, Lucioles, room 352
24/10/2024 9h00-12h30 Auto-Encoder and Latent Representation Michel Riveill Campus SophiaTech, Lucioles, room 352
31/10/2024 No lecture Break - -
07/11/2024 9h00-12h30 Recurrent Neural Network for time series Michel Riveill Campus SophiaTech, Lucioles, room 352
14/11/2024 9h00-12h30 Convolutional Neural Network 1 Diane Lingrand Campus SophiaTech, Lucioles, room 352
21/11/2024 9h00-12h30 Convolutional Neural Network 2 Diane Lingrand Campus SophiaTech, Lucioles, room 352
28/11/2024 9h00-12h30 No lecture -SophIA Summit Campus SophiaTech, Lucioles, room 352
05/12/2024 9h00-12h30 Object Detection Diane Lingrand Campus SophiaTech, Lucioles, room 352
12/12/2024 9h00-12h30 Recomender Systems and Lab Presentation Michel Riveill Campus SophiaTech,Lucioles, room 352