Managing Uncertainty, Frederic Pennerath

Managing Uncertainty

For any comment or request, send an email to Frederic Pennerath at centralesupelec.fr

Summary:

This course is about statistical models for Machine Learning and in particular Bayesian Machine Learning, that is the application of general and sound principles of Bayesian estimation to various machine learning problems.

Slides of the lectures

1. Introduction.
2. Theoretical Foundations.
3. Naive Bayes.
4. Gaussian Discriminant Analysis (QDA, LDA, etc).
5. Linear Models, reviewed.
6. Latent variables, EM and Mixture Models.
7. Discrete Markov models (HMM).
8. Continuous Markov models (KF and extensions, PF).
9. Example of non-parametric models: Gaussian Processes.