Romaric Gaudel

ADM — Analyse de Données et Modélisation probabiliste

M. Sc. course — Data analysis and probabilistic modeling

Making data speak: Advanced probabilistic data analysis and modeling

Disclaimer: Courses mainly based on Guillaume Gravier’s original course. I thank a lot Guillaume for the course material and for his numerous recommendations regarding the details of the course.

Data, whatever they are, are of very limited value without the possibility to extract valuable information to better synthesize, understand, predict. Statistical methods for data analysis and probabilistic models for statistical machine learning are commonly used to do so. This course aims at acquiring the basic techniques for data analysis (exploratory statistics) and probabilistic modeling (inferential statistics) and to study their application to different types of data (symbolic data, language, numerical data, signals, images, etc.). The lectures naturally articulate around the two major steps of any modeling process: understand your data then design an adequate model.

Keywords: data analysis, factor analysis, variance analysis, clustering, hypothesis testing, decision theory, estimation theory, Gaussian mixture models, EM algorithm, Markov chains, Markov fields, hidden Markov chains, Viterbi algorithm, Bayesian networks, token passing algorithm

Lectures, with the 2026-2027 dates

(Lectures are 1.5-hour sessions, held in Room Guernesey unless otherwise specified)
([prev.] indicates last year material which may be updated during the semester)

Evaluation / exams