The course covers the basics of computational statistics.

• Introduction (M-estimation, Z-estimation, maximum likelihood)
• Latent data models and Expectation Maximization algorithm
• Variational inference and Coordinate Ascent Variational Inference (CAVI)
• Basics on Makov chains and Metropolis-Hastings algorithm

• 1a - Maximum likelihood (Gamma distribution, Newton-Raphson update)
• 1b - Mixture models
• 2 - Introduction to MCMC algorithm
• 3 - Expectation Maximization
• 4 - Variational inference

• 1 - Gibbs sampling

• 2 - Introduction à l'algorithme EM

• 3 - EM pour les HMM discrets

• 4 - Algorithme CAVI

• Bonus - Maximum likelihood (Gamma distribution, Newton-Raphson update)