- Bayesian Network
- Belief Propagation
- Graphical Model
- Markov Random Field
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Expectation–Maximization (EM) Algorithm
Probabilistic Graphical Models Specialization
Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains
Skills you will gain
About this Specialization
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.
How the Specialization Works
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 3 Courses in this Specialization
Probabilistic Graphical Models 1: Representation
Probabilistic Graphical Models 2: Inference
Probabilistic Graphical Models 3: Learning
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