Minor Advanced AI: Advanced Machine Learning and Deep Learning

COORDINATOR

FORMAT / LOCATION

  • DS4H: Campus SophiaTech
  • SPECTRUM: campus Valrose

Prerequisites

CAPACITY

24 students

ABOUT THIS MINOR

This  minor is also open to students from the LIFE and SPECTRUM 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 text
Lecturers

Prerequisites
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
  • QCM on the course (50%)
  • Student Project (50%)

SCHEDULE

Autumn 2021 (last update: June, 11)
Each session includes a 60-90 min course and a 2-hour tutorial.
 

Date

Time slot

Room

Lecturer

Course title

Oct 14 8h30-9h00 Michel Riveill Course goals and outline
9h00- 12h30 Michel Riveill An introduction to Natural Language Processing
Oct 21 9h00- 12h30 Michel Riveill Deep learning – General principles
Oct 28 9h00- 12h30 Michel Riveill Deep learning - Multi-Layers perceptron
Nov 18 9h00- 12h30 Michel Riveill Deep learning - Recommender Systems
Nov 25 9h00- 12h30 Michel Riveill Deep learning - Recurrent Neural Network
Dec 2 9h00- 12h30 Diane Lingrand Deep learning - Convolutional Neural Network
Dec 9 9h00- 12h30 Diane Lingrand Deep learning – Model Explainability
Dec 16 9h00- 12h30 Diane Lingrand Deep learning - Reinforcement Learning