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MAPSI_TME_2022

MAPSI course at Sorbonne University

TME1 Probabilités et Statistiques

Distibution jointes, distribution conditionnelles, corélation, indépendance

TME2 Probabilités et Statistiques (suite)

Indépendance conditionnelle, Loi de Bernoulli/binomiale, Loi normale/Gaussien, PyAgrum

TME3 Maximum de Vraisemblance, Maximum a Posteriori

Maximum de Vraisemblance, Maximum a Posteriori, reconnaisance de chiffre, BD USPS, classification, matrice de confusion

TME4 Expectation-Maximization

EM, Old Faithful Geyser,Gaussien, animation, impact de l'initialisation, Dimension

TME5 Test d'indépendance

Indépendance conditionnelles et réseaux Bayésiens

TME6 Chaines de Markov

Classification de lettres manuscrites, matrice de confusion, modèle génératif de lettres manustrites

TME7 Chaine de Markov Cachés

Annotation de gènes par chaînes de Markov Caché, Forward Algorithm, Viterbi Algorithm, Forward-Backward Algorithm

TME8 Echantillonage

Diffusion dans les graphes

TM9 Régression & optimisation par descente de gradient

Fonction de coût & optimisation par descente de gradient, Sélection de caractéristiques, impacts des variables

TME10 Méthodes discriminantes

Max de vraisemblance, reconnaisance de chiffre, BD USPS, codage de la régression logistique -> système de classification

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