quatrième année

This course introduces the key principles of innovation and entrepreneurship, focusing on how ideas are generated, developed, and transformed into viable business opportunities. It covers creativity, opportunity recognition, business model design, and startup development. Students learn to evaluate ideas, understand market needs, and apply tools such as the Business Model Canvas. The course also highlights real-world challenges in launching and managing new ventures, fostering creativity, critical thinking, and an entrepreneurial mindset.

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.