Minor Artificial Intelligence: Introduction to Machine Learning

Previously entitled Minor AI (Introduction): Data Analysis and Machine Learning
Coordinated by: Michel Riveill, PR Université Côte d'Azur, Polytech, I3S

FORMAT

Hybrid

LOCATION

Campus SophiaTech, Lucioles + campus Valrose + remote sessions depending on the session (see schedule)

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
  • At the end of the course, students should be able to deal with basic problems in particular in the context of regression, classification, clustering or dimensionality reduction. They must also be able to explain the main differences between these different algorithms.
In the laboratories we will use the Python language and the sklearn library.
This minor gives you the keys to understanding the issues in the field and the tools to deal with simple data sets.​  
It emphasizes how an algorithm works and especially its use​ (it is not on the programmation of the algorithm​).
We will place ourselves from the point of view of a user​.
Machine learning and data analysis are increasingly at the centre of many sciences and applications. In this course, the fundamental principles and methods of machine learning will be introduced, analysed and put into practice. The main topics will be presented: linear and logistic regression, the principle of neural network functioning and the multilayer perception, unsupervised learning and some elements for working with text in machine learning.
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
  • Test at the beginning of the class (25 % of the final grade) - 10/11/2022
  • Test at the beginning of the class (25 % of the final grade) - 8/12/2022
  • 1 TD Project (50%)

SCHEDULE FALL 2022 (updated Oct. 3)

 
Mind the evaluation modalities and deadlines in the "Evaluation" tab above.
 
Date Time Course's title Lecturers Place
13/10/2022 9h00-10h30 General introduction
 - The different problems of ML
 - The learning process
Diane Lingrand Campus SophiaTech, Lucioles, room 348 + remote session
10h30-12h30 TP/TD (students with computer science advanced level) Diane Lingrand + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
20/10/2022 9h00-10h30 Regression with the linear model Diane Lingrand Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Diane Lingrand + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
27/10/2022 9h00-10h30 Classification - Régression logistique Diane Lingrand Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Diane Lingrand + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
10/11/2022 9h00-10h30 SVM Diane Lingrand Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Diane Lingrand + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
17/11/2022 9h00-10h30 SVM (cont'd) Diane Lingrand Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Diane Lingrand + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
24/11/2022 9h00-10h30 Dimension reduction (PCA, t-SNE) Michel Riveill Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Michel Riveill + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
01/12/2022 9h00-10h30 CART / Decision tree / Random Forest Michel Riveill Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Michel Riveill + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)
08/12/2022 9h00-10h30 Clustering (k-means, hclust) Michel Riveill Campus SophiaTech, Lucioles, room 348
10h30-12h30 TP/TD (students with computer science advanced level) Michel Riveill + Ali Ballout Campus SophiaTech, Lucioles, room 281
TP/TD (LIFE students) Edoardo Sarti Campus Valrose, room M03 M2.7 (coworking room)
TP/TD (other students) Hugo Schmutz Campus Valrose, room M03 M2.7 (coworking room)