Minor Introduction to Machine Learning

Coordinator

Michel Riveill

FORMAT / LOCATION

Campus SophiaTech, Sophia Antipolis

Prerequisites

Basic programming if possible with Python (see details below)

Capacity

24 students

ABOUT THIS MINOR

Summary

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.

LEARNING OUTCOMES

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​.
Prerequisites

Even if advanced knowledge of Python is not necessary to attend this thematic school you still need to know the basics of this programming language.

All the exercises will be given with the help of the notebook skirt. The most efficient way is to install it on your anaconda station (https://www.anaconda.com/products/individual). The proposed version installs everything necessary for the course.

Then if you do not practice Python on a daily basis,

 
Lecturers
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

SCHEDULE

Autumn 2021

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