Probabilistic Graphical Models 1: Representation
https://www.coursera.org/learn/probabilistic-graphical-models
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Categoria: Data Science
Subcategoria: Machine Learning
Tipo de Curso: Course
Habilidades: Bayesian Network,Graphical Model,Markov Random Field,
Idioma: English
Subtitulos: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Rating: 4.6stars
Vistas: 1.408
Sitio Web: Coursera
Duracion: Approx. 66 hours to complete
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