Course Overview

This lecture introduces fundamental concepts and associated numerical methods in model-based clustering, classification and models with latent structure. These approaches are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling.

Learning Achievement

Competence

Model-based clustering, classification and models with latent structure are particularly relevant to model random vectors, sequences or graphs, to account for data heterogeneity, and to present general principles in statistical modelling. The following topics are addressed:

Principles of probabilistic data mining and generative models; models with latent variables
Probabilistic graphical models
Mixture models and clustering
PCA and probabilistic PCA
Generative models for series and graphs : hidden Markov models

Course prerequisites

Fundamental principles in probability theory (conditioning) and statistics (maximum likelihood estimator and its usual asymptotic properties).
Constrained optimization, Lagrange multipliers.

Grading Philosophy

Final exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Jean-Baptiste Durand

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry