Bayesian Networks 1 - Inference - Stanford CS221: AI
Stanford University
60 Cours
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Aperçu
Le cours enseigne aux étudiants comment spécifier des distributions conjointes de manière compacte en utilisant des réseaux bayésiens et des graphes de facteurs. Il couvre l'inférence probabiliste, l'explication par élimination, la cohérence des sous-réseaux bayésiens, et des applications telles que le diagnostic médical, la modélisation du langage, le suivi des objets, le suivi de plusieurs objets, et la classification de documents.
Le cours vise à doter les apprenants des compétences nécessaires pour modéliser des systèmes complexes et prendre des décisions éclairées en utilisant la programmation probabiliste. Le public visé par ce cours est constitué de personnes intéressées par l'intelligence artificielle, l'apprentissage automatique et les modèles graphiques probabilistes.
Université : Université de Stanford
Fournisseur : Université de Stanford
Catégories : Cours d'Intelligence Artificielle, Cours de Statistiques & Probabilités, Cours d'Apprentissage Automatique
