Probabilistic Graphical Models 1: Representation
Coursera
60 Cours
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Aperçu
Découvrez les principes fondamentaux des Modèles Graphiques Probabilistes (PGM) avec "Modèles Graphiques Probabilistes 1 : Représentation", proposé par l'Université de Stanford via Coursera. Ce cours offre un regard approfondi sur le cadre riche des PGMs, une intersection critique de la statistique et de l'informatique, qui tire parti de la théorie des probabilités, des algorithmes graphiques, de l'apprentissage automatique, et plus encore. Conçu comme la première partie d'une séquence de trois cours, il couvre les essentiels des Réseaux Bayésiens et des réseaux de Markov, leurs fondements théoriques, et applications pratiques.
Les PGMs servent de colonne vertébrale pour les méthodes de pointe dans un large éventail de domaines, y compris le diagnostic médical, la compréhension d'image, la reconnaissance de la parole, le traitement du langage naturel, et au-delà. Ce cours n'introduit pas seulement les apprenants aux représentations de base au sein des PGMs mais explore également des extensions importantes permettant de coder des modèles plus complexes de manière efficace. Les participants intéressés par une expérience pratique trouveront la voie d'honneurs optionnelle particulièrement avantageuse, car elle comprend plusieurs devoirs qui appliquent ces concepts à des problèmes réels.
Tombant sous plusieurs catégories telles que l'Intelligence Artificielle, les Statistiques & Probabilités, l'Apprentissage Automatique, et les Cours d'Informatique, cette offre est essentielle pour quiconque cherche à approfondir sa compréhension des modèles probabilistes complexes et leur application dans la technologie d'aujourd'hui.
Programme
Enseigné par
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Coursera
Coursera
1275 Cours
Coursera
Free Online Course (Audit)
English
67 hours
On-Demand
