Advanced Machine Learning course
- Emilie Chouzenoux, CVN, CentraleSupélec and OPIS team, Inria
- Frederic Pascal, L2S, CentraleSupélec, Univ. Paris-Saclay
- Alix Chazottes
- Houssam Zenati
Summary
This course is an advanced course focusing on the intersection of Statistics and Machine Learning. The goal is to study modern statistical methods for supervised and unsupervised learning and the underlying theory for those methods. Numerous illustrations in the context of signal/image processing will be provided through programming lab sessions in Python language.
Labs should be sent to the following email addresses: alix.chazottes@centralesupelec.fr or houssam.zenati@inria.fr
Outline
- Course 1: Introduction - Reminders of probability theory and mathematical statistics (Extreme Value Theory, Bayes, estimation, discriminant analysis, SVM) [Slides] [Data Viz] [Use case]
- Course 2: Linear regression / linear classification [Slides] - Lab session [Slides] [Notebook] [Data]
- Course 3: (Hierarchical) Clustering [Slides] - Lab session [Slides] [Notebook] [Data] [Data2]
- Course 4: Stochastic Gradient Descent [Slides] - Lab session [Slides] [Notebook] [Data]
- Course 5: Mixture Models and Model Order Selection [Slides] [Application]- Lab session [Slides] [Notebook]
- Course 6: Non-negative Matrix Factorization (NMF) [Slides] - Lab session [Slides] [Notebook]
- Course 7: Inference on Graphical Models - Lab session [Slides] [Notebook]
The course bibliography can be found here: [Biblio 23/24]
