This course examines the foundations of governance in digital environments, clarifies the distinction between digitization, digitalization, and digital transformation, and analyzes digital systems as socio-technical structures embedded in organizational and legal frameworks. Particular attention is given to the Algerian context, in dialogue with international governance standards.
L’un des concepts les plus stratégiques sur lequel le cloud CloudComputing se base est le concept de virtualisation. Les services fondamentaux du cloud tels que le PaaS et le Iaas sont les services les plus touchés par le concept de virtualisation. Ces services font face à des défis importants tels que le redimensionnement des machines virtuelles suite à des besoins dynamiques de l’utilisateur, l’augmentation des ressources des machines virtuelles en termes de CPU, mémoire ou autre ressource. L’objectif de ce cours esde montrer d’une part l’évolution des technologies et des défis auxquels ont fait face les systèmes informatiques pré-cloud, notamment le clustering et palper ensuite d’une part
les différences fondamentales entre les cluster et les cloud et d’autre part les défis et les problèmes techniques auxquels font face les infrastructures (les data center) du cloud
This master’s course equips students with advanced theoretical and practical competencies to model, quantify, and reason under uncertainty in intelligent systems. Participants will learn to distinguish and address different forms of uncertainty through probabilistic graphical models, sequential state‑space methods, and fuzzy logic, while also extending deep learning architectures to Bayesian formulations for reliable predictive confidence. Emphasis is placed on algorithmic implementation, critical evaluation of research literature, and the ability to design uncertainty‑aware solutions for real‑world, safety‑critical applications—preparing graduates for both doctoral research and industry roles that demand robust and transparent AI systems.
The primary objectives of this Deep Learning course are to equip Master’s students with a solid theoretical and practical foundation in neural networks, starting from the mathematical modeling of artificial neurons and progressing to the principles of learning and adaptation. Students will learn to train multilayer perceptrons, diagnose and mitigate overfitting and underfitting, and apply deep learning techniques to classification tasks. The course further aims to develop proficiency in advanced architectures, including Convolutional Neural Networks (CNNs) for visual data, and recurrent models such as RNNs, LSTMs, and encoder-decoder structures for sequential processing, enabling students to design, implement, and evaluate deep learning solutions across diverse application domains.
- Learn the basics of computer vision.
- Introduce the student to apply computer vision techniques to real-world problems.
- Apply image processing, Machine Learning, and Deep Learning techniques to Computer Vision problems.