Mathématiques Avancées pour la Science des Données

.The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This course covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It provides great coverage of all the basic mathematical concepts for data-scientist looking for building a solid understanding of the fundamentals.

Part I: Mathematical Foundations

1. Introduction and Motivation
2. Linear Algebra
3. Analytic Geometry
4. Matrix Decompositions
5. Vector Calculus
6. Probability and Distribution
7. Continuous Optimization

Part II: Central Machine Learning Problems

1. When Models Meet Data
2. Linear Regression
3. Dimensionality Reduction with Principal Component Analysis
4. Density Estimation with Gaussian Mixture Models
5. Classification with Support Vector Machines.

Consultez les ressources disponibles concernant ce module sur le moteur de recherche de la bibliothèque, ou accédez directement au cours de vos enseignants via la plateforme de téléenseignement de l’école « e-learn ».