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.
Ce cours fait partie de la Spécialisation Modèles graphiques probabilistes
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À propos de ce cours
Compétences que vous acquerrez
- Bayesian Network
- Graphical Model
- Markov Random Field
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Programme de cours : ce que vous apprendrez dans ce cours
Introduction and Overview
Bayesian Network (Directed Models)
Template Models for Bayesian Networks
Structured CPDs for Bayesian Networks
Markov Networks (Undirected Models)
Decision Making
Avis
- 5 stars74,76 %
- 4 stars17,74 %
- 3 stars5,20 %
- 2 stars0,99 %
- 1 star1,28 %
Meilleurs avis pour PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.
learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..
À propos du Spécialisation Modèles graphiques probabilistes

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Learning Outcomes: By the end of this course, you will be able to
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