Algorithmic & Programming

The objective of this course is to introduce students to the basics of imperative programming which is one of the most effective paradigms of modern programming. Thus, the theme of algorithmic will be discussed to prepare the continuation of the course. Indeed, an algorithm expresses the logical structure of a solution to a given problem and this independently of any language and any machine. Algorithmic is one of the key steps in teaching programming. Then, it is a matter of applying the knowledge approached in algorithmic through the mastery of a programming language. This is precisely the purpose of this second part of this module which aims to equip students with the background necessary to master a given programming language, in this case the C language, which is at the base of many others. Our objectives are summarized as follows:

• Learn the basic concepts of algorithmic
• Be able to implement these concepts to analyze simple problems
• Mastering abstraction mechanisms in order to analyze a problem and systematically design correct and adequate algorithms and programs
• Implement, using the C language, the algorithms solving a particular problem
• Master the basics of modular programming

1090 Students
Enseignant: Ali Berro
Enseignant: Kamal Beydoun
Enseignant: Ibrahim Bitar
Enseignant: Marwan Cheaitou
Enseignant: Zein Ibrahim
Enseignant: Mohammad Smaili
Enseignant: Antoun Yaacoub
Lab Assistant: Hadi Al Khansa
Lab Assistant: Mustafa Al Muhammad
Lab Assistant: Mehieddine Al Nakouzi
Lab Assistant: Mohiddeen AL-Barazi
Lab Assistant: Hussein Allaw
Lab Assistant: Ali Youssef Khalil
Lab Assistant: Medyan Mehi El Dine
Lab Assistant: Wassim Okasha
Lab Assistant: Salma Sweid
Lab Assistant: Ali Sweidan

Imperative Programming

The objective of this module is to deepen the study of the imperative programming through the use of advanced constructions of the imperative language seen in I1101. The student must be able to apply the concepts seen in this course to create an application solving a complex problem in the form of modules.

320 Students
Enseignant: Ali Berro
Enseignant: Ibrahim Bitar
Enseignant: Doreid Dagher
Enseignant: Danielle Dheiny
Enseignant: Siba Haidar
Enseignant: Zein Ibrahim
Enseignant: Ali Noureldin
Enseignant: Mohammad Smaili
Enseignant: Antoun Yaacoub

Graph Theory

The intended learning outcome of this course is to give good knowledge of graph theoretical concepts, and to practice how to use them in mathematics, natural science and computer science.

After completing the course, students should be able to:

• know some important classes of graph theoretic problems;
• be able to formulate central theorems about trees, matching, connectivity, colouring and planar graphs;
• be able to describe and apply some basic algorithms for graphs;
• be able to use graph theory as a modelling tool.

215 Students
Enseignant: Siba Haidar
Enseignant: Antoun Yaacoub

Functional Programming - Python

This course is designed: 

• to give students a thorough understanding of the Python programming language and it's rich set of libraries;
• to expose students to applications where Python programming is effective, and 
• to introduce students to pros and cons of scripting vs. compiled programming languages. 

Python is an interpreted, inherently object oriented dynamic language which has been gaining popularity for the past several years. It’s widely used by organizations such as NASA, Google and Industrial Light and Magic, among others. 

It has easy to understand syntax which allows programmers to develop programs faster and be more efficient. Python has proven to be equally as useful for small scripts as well as large scale software systems. 

Due to Python’s ease of use, students will gain expertise with many details of the language as well as programming fundamentals in a short period of time.

131 Students
Enseignant: Antoun Yaacoub

Data Structures

Students learn to develop applications where complete implementation requires high skills in structured programming domain. The main objectives are to specify, conceive and develop modules implementing the fundamental data structures and to choose the most appropriate data structure to represent data in a given problem and to allow its resolution by applying efficient and optimal operations.

344 Students
Enseignant: Ali Berro
Enseignant: Siba Haidar
Enseignant: Zein Ibrahim
Enseignant: Antoun Yaacoub

Advanced Algorithms

This course explores advances in algorithm design, algorithm analysis and data structures. The primary focus is on advanced data structures. Topics include advanced trees structures, disjoint sets, heaps, algorithm design techniques, data structures for strings and linear programming algorithms. Finally, we discuss NP-completeness.

11 Students
Enseignant: Siba Haidar
Enseignant: Antoun Yaacoub

Inferential Statistics

74 Students
Enseignant: Zainab Assaghir
Enseignant: Layal Elhajj

Regression models

70 Students
Enseignant: Zainab Assaghir

Python for Generative AI Practitioners

Ce cours intensif de 30 jours est conçu spécifiquement pour les étudiants du MSc en IA Générative qui n'ont pas de background technique. Loin des cours de développement logiciel classiques, nous nous concentrons ici sur une compétence unique : la "Litératie du Code" pour l'IA. Vous n'apprendrez pas à développer des applications web, mais à lire, comprendre, modifier et déboguer les Jupyter Notebooks que vous utiliserez quotidiennement pour piloter des LLMs, générer des images ou préparer des données.

L'approche est résolument pratique : "Pas de théorie sans pratique immédiate". Chaque module quotidien vous prendra entre 15 et 20 minutes. Il est composé d'un concept clé, d'exemples interactifs modifiables directement dans le navigateur (via CodeRunner), et d'un quiz adaptatif pour valider vos acquis. À la fin de ces 30 jours, vous ne regarderez plus jamais une cellule de code Python avec appréhension, mais comme un panneau de contrôle que vous savez manipuler.

9 Students

OneClickQuiz

11 Students
Enseignant: Test user 1
Enseignant: Test user 2

Gen AI - Enseignants

10 Students
Enseignant: Radwan Almokdad
Enseignant: bassem.haidar@esiea.fr bassem.haidar@esiea.fr
Enseignant: felicia.ionascu@esiea.fr felicia.ionascu@esiea.fr
Enseignant: Siba Haidar
Enseignant: jean-pierre.aubin@esiea.fr jean-pierre.aubin@esiea.fr
Enseignant: jerome.darugna@esiea.fr jerome.darugna@esiea.fr
Enseignant: Edouard Nadaud
Enseignant: pierre.courbin@esiea.fr pierre.courbin@esiea.fr
Enseignant: thibault.geoffroy@esiea.fr thibault.geoffroy@esiea.fr
Enseignant: Antoun Yaacoub

Machine Learning

8 Students

HCIA-AI

This course is HCIA-AI V3.0. Through this course, you will systematically understand the AI development history, the Huawei Ascend AI system, the full-stack all-scenario AI strategy, and the algorithms related to traditional machine learning and deep learning; TensorFlow and MindSpore.

1 Students
Enseignant: Antoun Yaacoub

Final exams

149 Students
Enseignant: Siba Haidar
Enseignant: Zein Ibrahim
Enseignant: Antoun Yaacoub