Traitement Automatique du Langage Naturel

The course covers many topics, including preprocessing, text representation and classification, syntactic and semantic analysis, sentiment analysis, and language modeling. It allows students to apply different NLP techniques to deal with practical problems such as machine translation, text summarization, information extraction, information retrieval and question answering.

Deep Learning

The Deep Learning course aims to provide a thorough understanding of deep neural network architectures and advanced machine learning techniques. Objectives include learning key concepts such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and the
implementation and optimization of these models to solve complex problems in image, text, and sequential data processing.

Analyse et traitement d’image

Ce cours traite de l'acquisition, du traitement et de l'analyse d'images numériques réelles. Il s'agit de modéliser les processus nécessaires à la transformation de ces images discrètes (ensemble de pixels) en donnés perceptuelles et sémantiques (scène d'objets). Ce cours traite en particulier les problèmes d'acquisition.

Big Data Processing

This course aims to provide the fundamental concepts and practical skills required for large-scale data processing. It focuses on hosting massive data on distributed clusters and performing parallel and real-time data processing. 

Data Security

This course complements the "Introduction to Computer Security" course, it offers a comprehensive overview of contemporary cybersecurity mechanisms. It emphasizes foundational concepts, prevailing threats, and practical defense strategies, and prepares students to assess risks, analyze adversarial behavior, and implement effective security controls to safeguard systems and data across diverse environments, including cloud infrastructures, emerging technologies, and data-intensive platforms.

Technologies de Calcul Distribué

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Mettre l’accent sur l’apport de l’IA dans les nouvelles technologies à commencer par un bref historique sur les grilles de calculs pour mettre en avant leur intérêt dans la parallélisation et la distribution des calculs en IA. Pour ensuite, aborder les solutions IA pour répondre aux problématiques du Cloud, Fog et Edge computing, et finir par la concrétisation de l’IoT.