Session Janvier 2026

Ce module est destiné aux étudiants de première année des Écoles Normales Supérieures. Il vise principalement à renforcer les acquis antérieurs en mathématiques et à assurer la transition avec les exigences de l’enseignement universitaire, en comblant les lacunes éventuelles.

Sur le plan du contenu, le module s’articule autour de trois axes principaux :

• Le raisonnement et la démonstration mathématique : notions de logique, types de raisonnement et méthodes de preuve (récurrence, contradiction, contre-exemple, etc.).

• Les activités numériques : étude des ensembles de nombres (ℕ, ℤ, ℚ, ℝ), opérations, divisibilité, nombres premiers, division euclidienne et résolution d’équations.

• Les activités géométriques : bases de la géométrie euclidienne, étude des figures (triangles, cercles), vecteurs et équations de la droite et du cercle.

Sur le plan pédagogique, le module repose sur :

• une progression du simple au complexe,

• l’articulation entre théorie et pratique,

• la diversification des exercices (fermés et ouverts),

• l’implication active des étudiants dans la recherche, la discussion et l’analyse.

L’évaluation se fait à travers :

• des questions orales,

• des travaux à domicile,

• des contrôles écrits.

Dans l’ensemble, ce module vise à développer chez l’étudiant une pensée mathématique rigoureuse, en combinant compréhension théorique, pratique et capacité de raisonnement.

The Basic Mathematics course is a core 

learning module designed for first-year

university students in Arabic language.

This course aims to bridge the gap between

secondary school knowledge and university

requirements, with a focus on developing

sound mathematical reasoning skills.

Course Description:

This course provides an in-depth introduction to the fundamental concepts of knowledge representation and reasoning in Artificial Intelligence. It explores various approaches used to model, structure, and exploit information within intelligent systems, including non-classical logics, probabilistic reasoning, and fuzzy logic.

Special emphasis is placed on understanding how intelligent systems make decisions in complex, uncertain, or incomplete environments. Through practical examples, exercises, and interactive activities, students will learn how to design formal models and select appropriate reasoning techniques depending on the context.

This course serves as a foundational component for the development of intelligent systems and prepares students for advanced applications in artificial intelligence, data science, and decision-support systems.

Target Audience:

This course is intended for Master’s students in Artificial Intelligence and 4th-year engineering students in Computer Science, with prior knowledge in logic, discrete mathematics, and basic AI concepts.

Learning Objectives

At the end of this course, students will be able to:
Identify the essential concepts of knowledge representation and reasoning in Artificial
Intelligence, through short conceptual questions and guided examples.
Differentiate classical logic from non-classical logics, by correctly classifying rea-soning
situations from simple Artificial Intelligence scenarios.
Interpret the main formalisms studied in this course, namely modal logic, default log-ic, and
description logics, through symbolic expressions and structured exercises.
Compare the main approaches to uncertain reasoning, particularly Dempster–Shafer Theory
and Bayesian Networks, by identifying their characteristics and fields of application in
guided case studies.
Choose the most suitable reasoning method for a simple Artificial Intelligence problem, by
justifying your choice based on the nature of the represented knowledge.