Graduate School and Research Center in Digital Sciences

Machine Learning and Intelligent System

T Technical Teaching


The objective of this course is to give students a solid background in Machine Learning (ML) techniques. ML techniques are used to build efficient models for problems for which an optimal solution is unknown. This course will introduce the basic theories of Machine Learning, together with the most common families of classifiers and predictors. It will identify the basic ideas underlying the mechanism of learning, and will specify the practical problems that are encountered when applying these techniques, optimization, overfitting, validation, together with possible solutions to manage those difficulties.

Teaching and Learning Methods : Lectures and Lab sessions (groups of 1 or 2 students)

Course Policies : Attendance to Lab sessions is mandatory.


Suggestions are provided on the intranet.


 No prerequisite


   Machine Learning basics

    Genetic Algorithms

    Neural Networks

    Support Vector Machines

    Decision Trees

Learning outcomes:

-      Master the fundamentals of Machine Learning

-      Understand the most common families of classifiers

-      Be able to apply ML algorithms to practical problems

Nb hours:42.00, including 3 Lab sessions (9 hours)

Grading Policy: Final Exam (100%)

Nb hours: 42.00
Nb hours per week: 3.00