Cours à distance

ECTS Maths : 6    ECTS Info : 3    Cours de Période 1   

• Teacher Stéphane Gaiffas
• Website of the course https://stephanegaiffas.github.io/teaching/m2mo
• Link to the course's Moodle
• Prérequis Optimisation M1. Statistique M1
• Modalités de validation du cours Contrôle continu + examen
• Volume horaire du cours 3h de cours + 2h de travaux pratiques par semaine
• Durée 10 semaines

Syllabus

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn from data. A major focus of machine learning is to automatically learn complex patterns and to make intelligent decisions based on them. The set of possible data inputs that feed a learning task can be very large and diverse, which makes modeling and prior assumptions critical problems for the design of relevant algorithms.

This course focuses on the methodology underlying supervised and unsupervised learning, with a particular emphasis on the mathematical formulation of algorithms, and the way they can be implemented and used in practice. The course will describe for instance some necessary tools from optimization theory, and explain how to use them for machine learning. Numerical illustrations and applications to datasets will be given for the methods studied in the course. Practical sessions will start with a presentation of the Python language and of the main librairies for data science and scientific computing.