Teaching

This course introduces fundamental machine learning techniques, combining theoretical understanding with practical applications. Each session consists of a lecture and a lab, and will start with a quiz on the previous session.

Objectifs d’apprentissage Savoir développer, avec SQL, PL/pgSQL et un langage de programmation (Java ou Python), une application complète reposant sur un SGBD relationnel, en prenant en compte les contraintes d’intégrité, la gestion des transactions, la confidentialité des données et l’indépendance logique/fonctionnelle.

This course provides a (really) brief introduction to the foundational concepts of neural networks, their structure, functioning, and applications in machine learning. Course Structure 1 lecture (3 hours): Slides Overview of neural network basics: layers, weights, biases, and activation functions.

This course introduces key algorithmic techniques for solving large-scale data science problems, focusing on efficient data processing and analysis methods. Students will explore fundamental topics such as frequent itemset mining, mining similar items, and data stream algorithms.

This course introduces the fundamentals of data warehousing, covering key concepts, architectures, and practical implementations using open-source tools. The course is designed for master’s students with diverse backgrounds and prepares them for an advanced Data Warehouse course.