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Informatique et Mathématiques appliquées
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Probabilistic models for learning - 4MMMPA6

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  • Number of hours

    • Lectures : 16.5
    • Tutorials : 16.5
    • Laboratory works : -
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 3.0
  • Officials : Olivier FRANCOIS

Goals

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.

Content

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.

Prerequisites

Basics in probability theory and statistics.

Tests

Written exam and homework.

E1 = (2 * N1 + TP)/3
E2 = N2
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Calendar

The course exists in the following branches:

  • Curriculum - Math. Modelling, Image & Simulation - Semester 7
see the course schedule for 2020-2021

Additional Information

Course ID : 4MMMPA6
Course language(s): FR

The course is attached to the following structures:

You can find this course among all other courses.

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Date of update January 15, 2017

Université Grenoble Alpes