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 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.
| 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 Scientist-Machine 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 | |
|---|---|---|
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Jul |
Probabilistic Graphical Models 1: Representation (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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Jun |
Probabilistic Graphical Models 2: Inference 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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Jul |
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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| 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 Scientist-Machine 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
