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Stanford University

Probabilistic Graphical Models

Stanford University via Coursera Specialization

Overview

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.

Syllabus

Course 1: Probabilistic Graphical Models 1: Representation
- Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.

Course 2: Probabilistic Graphical Models 2: Inference
- Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.

Course 3: Probabilistic Graphical Models 3: Learning
- Offered by Stanford University. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over ... Enroll for free.

Courses

Taught by

Daphne Koller

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