Advanced Machine Learning
Instructors
Emilie
CHOUZENOUX, Inria Saclay, CVN
Frédéric PASCAL, L2S, CentraleSupelec
Teaching Assistant
Ludovic Trautmann, Inria Saclay
Course 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.
A selective bibliography can be found in [PDF]
Course outline
The course consists of eight sessions (3h each) combining lectures and
exercices. The following concepts will be presented:
- 1. Reminders on ML and Bayesian theory
[Slides][Visualisation]
- 2. Linear regression/classification approaches
[Slides]
[Lab]
- 3. Hierarchical clustering
[Slides]
[Lab]
- 4. Stochastic approximation algorithms
[Slides]
[Lab]
- 5. Nonnegative matrix factorization
[Slides]
[Lab]
- 6. Mixture models fitting and model order selection
[Slides]
[Lab]
- 7. Inference on graphical models
[Slides]
[Lab]
- Article for the exam 2025/2026
[Article]
- Exam of past years
[Subject]
[Subject]
- Corrections of labs
[Link]
- Instructions for lab:
Lab instructor: Ludovic Trautmann
Lab notebooks must be sent to: ludovic.trautmann@inria.fr.
- Emilie Chouzenoux -
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