Spécialisation Probabilistic Graphical Models

Commence le Oct 17

Spécialisation Probabilistic Graphical Models

Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains

À propos de cette Spécialisation

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.

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courses
3 courses

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Vue d'ensemble des projets

Cours
Advanced Specialization.
Designed for those already in the industry.
  1. COURS 1

    Probabilistic Graphical Models 1: Representation

    Sous-titres
    English

    À propos du cours

    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 representation
  2. COURS 2

    Probabilistic Graphical Models 2: Inference

    Sous-titres
    English

    À propos du cours

    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 representation
  3. COURS 3

    Probabilistic Graphical Models 3: Learning

    Sous-titres
    English

    À propos du cours

    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 representation

Créateurs

  • Stanford University

    Stanford University is one of the world's leading teaching and research universities. Since its opening in 1891, Stanford has been dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world.

    The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

  • Daphne Koller

    Daphne Koller

    Professor

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