Minor Introduction to AI: Data Analysis and Machine Learning

COORDINATOR

FORMAT / LOCATION

  • DS4H: Campus SophiaTech
  • LIFE and SPECTRUM: campus Valrose

Prerequisites

Scientific Bachelor and Python programming (see details below)

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 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

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

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 Rodrigo Cabral Farias General introduction: the different problems of ML, the learning process
Oct 21 9h00- 12h30 Rodrigo Cabral Farias Regression with the linear model
Oct 28 9h00- 12h30 Rodrigo Cabral Farias Classification - Régression logistique
Nov 18 9h00- 12h30 Rodrigo Cabral Farias SVM
Nov 25 9h00- 12h30 Lionel Fillatre LDA / Naive Bayes
Dec 2 9h00- 12h30 Lionel Fillatre CART / Decision Tree / Random Forest
Dec 9 9h00- 12h30 Michel Riveill Clustering (k-means, hclust)
Dec 16 9h00- 12h30 Michel Riveill Dimension reduction (PCA, t-SNE)