Minor AI (Introduction): Data Analysis and Machine Learning

Coordinated by: Michel Riveill, PR Université Côte d'Azur, Polytech, I3S

FORMAT

Hybrid

LOCATION

  • Campus SophiaTech
  • Campus Valrose
  • Remote courses

Prerequisites

Scientific Bachelor and Python programming (see details below)

CAPACITY

24 students

ABOUT THIS MINOR

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

Summary

LEARNING OUTCOMES

  • Know the principles of Machine Learning, the main classes of problems, the main models
  • Know how to use the tools of the domain to analyze data that do not require pre-processing
Lecturers

PhD Students for TP/TD supervision

Prerequisites
If you do not practice Python on a daily basis,
 
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

This minor is not open this semester.