Stanford University - Probabilistic Graphical Models Specialization
- Offered byCoursera
- Public/Government Institute
Probabilistic Graphical Models Specialization at Coursera Overview
Probabilistic Graphical Models Specialization
at Coursera
Duration | 4 months |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Probabilistic Graphical Models Specialization
Table of content
Probabilistic Graphical Models Specialization at Coursera Highlights
Probabilistic Graphical Models Specialization
at Coursera
- Earn a Certificate upon completion
Skills -Inference,Bayesian Network,Belief Propagation,Graphical Model,Markov Random Field,Gibbs Sampling,Markov Chain Monte Carlo (MCMC)
Probabilistic Graphical Models Specialization at Coursera Course details
Probabilistic Graphical Models Specialization
at Coursera
Skills you will learn
More about this course
- 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
- Applied Learning Project
- Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models
- This specialization has three five-week courses for a total of fifteen weeks.
Probabilistic Graphical Models Specialization at Coursera Curriculum
Probabilistic Graphical Models Specialization
at Coursera
Probabilistic Graphical Models 1: Representation
Probabilistic Graphical Models 2: Inference
Probabilistic Graphical Models 3: Learning
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