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.
This course is part of the Probabilistic Graphical Models Specialization
About this Course
Skills you will gain
- Bayesian Network
- Graphical Model
- Markov Random Field
Syllabus - What you will learn from this course
Introduction and Overview
Bayesian Network (Directed Models)
Template Models for Bayesian Networks
Structured CPDs for Bayesian Networks
Markov Networks (Undirected Models)
- 5 stars74.60%
- 4 stars17.70%
- 3 stars5.33%
- 2 stars1.06%
- 1 star1.28%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
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.
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.
concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos
About the Probabilistic Graphical Models Specialization
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Learning Outcomes: By the end of this course, you will be able to
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