Teaching

Advanced Machine Learning 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.

Outline:
  • Course 1: Introduction - Reminders of probability theory and mathematical statistics (Bayes, estimation, tests) [Slides]
  • Course 2: Linear regression/classification approaches [Slides] [TP] [Datasets]
  • Course 3: Stochastic approximation algorithms
  • Course 4: Hierarchical clustering
  • Course 5: Nonnegative matrix factorization
  • Course 6: Mixture models fitting
  • Course 7: Model order selection
  • Course 8: Inference on graphical models

Advanced Statistical Methods

Syllabus:
This course aims first at introducing the general methodology of mathematical statistics through the fundamental concepts (statistical modelling and sampling, estimation problems, decision theory and hypothesis testing). Then, this course provides advanced statistical techniques for multivariate analysis with a particular focus on computational statistics and robust estimation approaches. Regularized / penalized techniques are also presented.

Outline:
  • Introduction [Slides]
  • Part A: Reminders of Probability Theory and Mathematical Statistics [Slides]
  • Part B: Statistical Modelling and Parameter Estimation Theory [Slides]
  • Part B: Applications [Slides]
  • Part C: Hypothesis Testing - Decision Theory [Slides]
  • Part C: Applications [Slides]

Support for the course of probabilities (in french) [.pdf]