Teaching

This course introduces fundamental machine learning techniques, combining theoretical understanding with practical applications. Each session consists of 1.5 hours of lecture and 1.5 hours of practical exercises, which will be graded individually.

This course is a (really) brief introduction to neural networks. Course Structure 1 lecture (3 hours) 1 session of exercises (3 hours) Lecture Slides Exercises Statements Required Tools (Open-source and Free) A recent version of Python and the following packages: numpy pandas scikit-learn matplotlib setuptools

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.

Structure du cours Le cours est découpé en deux : Bases de données 2 et Bases de données 2 avancées. Bases de données 2 L’énoncé de TD pour l’ensemble de ce cours est disponible ici.

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.