About this Specialization
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 stateoftheart 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.&
From  Stanford University via Coursera 

Hours  72 
Instructor  Daphne Koller 
Language  English 
Subjects  Programming Data Science 
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Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile (33rd  99th).
Research ScientistMachine Learning $55k
Cloud Architect  Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist  Machine Learning ... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
Courses in this Specialization
Listed in the order in which they should be taken
Starts  Course Information  

Aug 
Probabilistic Graphical Models 1: Representation Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of... Coursera  Stanford University
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Aug 
Probabilistic Graphical Models 2: Inference (You were viewing this course) Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of... Coursera  Stanford University
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Complete this course for only $79
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Aug 
Probabilistic Graphical Models 3: Learning Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of... Coursera  Stanford University
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Complete this course for only $79
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Take just this course For $79
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&
From  Stanford University via Coursera 

Hours  72 
Instructor  Daphne Koller 
Language  English 
Subjects  Programming Data Science 
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile (33rd  99th).
Research ScientistMachine Learning $55k
Cloud Architect  Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist  Machine Learning ... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
Similar Courses
Sorted by relevance