Number of hours
- Lectures 16.5
- Projects -
- Tutorials 16.5
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 3.0
Goal(s)
The objective of the course is to provide students with basic knowledge and skill in probabilistic models for statistical and machine learning applications. Teaching focuses on concepts of statistical dependence and algorithms for analysis complex and structured data, with a Bayesian perspective. Teaching language is french.
Olivier FRANCOIS
Content(s)
The first part of the course deals with concepts of statistical dependence statistique, covariance, Gaussian vectors and linear regression models(6 weeks). The second part of the course presents an introduction to Bayesian data analysis and to Bayesian algorithms such as Markov chain Monte-Carlo methods (6 weeks). Applications to classification using mixture models are studied.
PrerequisitesBasics in probability theory and statistics.
Written exam and homework.
- MCC en présentiel **
N1 = 1/2 TP + 1/2 examen écrit
N2 = examen écrit ou oral
- MCC en présentiel **
- MCC en distanciel **
N1 = 1/2 TP + 1/2 examen a distance
N2 = écrit ou oral à distance
- MCC en distanciel **
Documents autorisés
The course exists in the following branches:
- Curriculum - Math. Modelling, Image & Simulation - Semester 7
Course ID : 4MMMPA6
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