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This course builds on the first semester’s English Communication module and aims to further develop students’ ability to use English confidently and naturally in real-life situations. Through topics such as travel, shopping, technology, communication, and social interactions, students expand their vocabulary, improve grammatical accuracy, and strengthen fluency in spoken English. The course emphasizes practical communication skills, including making polite requests, expressing opinions, describing experiences, and participating in extended conversations, while integrating listening and speaking activities to enhance coherence, interaction, and overall communicative competence.

This course provides a comprehensive set of topics and lessons for students between A2 and B1 levels, with a focus on communication in daily life. Each lesson includes structured activities that integrate speaking, listening, and grammar practice to help students improve their English fluency in a variety of everyday situations

Course Description 

The Operating Systems: Synchronization and Communication course delves into the fundamental concepts of parallelism and concurrency within operating systems. It examines essential mechanisms for process synchronization and inter-process communication (IPC), along with the primary challenges of concurrent execution. Key topics include mutual exclusion, process synchronization, communication models, monitors, and deadlock management. Practical aspects emphasize hands-on UNIX implementations using tools such as processes, signals, semaphores, shared memory, message queues, and pipes.

Target Audience: 3rd-year Artificial Intelligence Engineering students 

Operating System Security refers to the set of:

• Architectural mechanisms

• Enforcement policies

• Verification chains

that collectively ensure:

• Controlled access to system resources

• Strong isolation between execution contexts

• Integrity of the kernel and trusted components

• Resistance to privilege escalation and persistence attacks

Course Description : This course provides a comprehensive introduction to deep learning, covering fundamental concepts, architectures, and advanced techniques. Students will explore the theoretical foundations and practical applications of neural networks, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models. The course emphasizes hands-on experience through practical work sessions where students implement deep learning models using modern frameworks such as TensorFlow and PyTorch.

Target Students: 4th Ing. AI

This course aims to develop the behavioral and interpersonal competencies that are essential for engineering students in both academic and professional environments. Through a combination of short theoretical inputs, practical exercises, and real-world simulations, students will learn how to communicate effectively, collaborate in teams, solve problems critically, present technical ideas persuasively, and demonstrate soft skills in recruitment and career contexts. At the end of the course, students are expected to understand the relevance of soft skills in engineering careers and to apply them confidently in real situations.

This course introduces students to the principles of modeling computer networking behaviors to evaluate their performance. It concentrates on the queuing theory and probabilistic models, especially Markov chains, to be exploited in the modeling process. At the end of this course, students will be able to manipulate probabilistic models and Markov chains for evaluating computer network performances.

L’objectif de ce cours est :

- de donner un aperçu sur les méthodes de la Recherche Scientifique,

- d'initier les étudiants aux normes de rédaction de mémoire et

- comment préparer et soutenir son travail de Master.

Il comporte deux parties :

Partie 1 : Méthodes de la Recherche Scientifique 

Partie 2 : Rédaction de Mémoire & Soutenance Orale

Permettre aux étudiants de s’initier aux principales méthodes de recherche, de mener correctement un projet de recherche, et de savoir communiquer les résultats de la recherche.

students are expected to gain comprehensive knowledge and practical skills in several key areas. Firstly, they will learn the fundamentals of cloud computing, service models and deployment models. The course will delve into virtualization technologies, which are the backbone of cloud services, explaining how they enable the efficient use and allocation of computing resources. A significant focus will be on OpenStack, an open-source platform for cloud computing.

English course

This course introduces students to the main concepts and tools of Business Intelligence (BI). It covers the full BI process; from data integration using ETL and data warehousing to multidimensional databases, reporting, and data mining. Students will explore how BI supports strategic decision-making and business performance through practical examples such as customer relationship management (CRM). #Basic knowledge of information systems and databases is recommended.