Current
Intermediate
40% Off
Probabilistic Graphical Models Specialization
Coursera · Stanford University · Updated March 2026
Platform rating
4.3/5
AI Tutor Rating
7.8/10
Duration
4-8 months
Classes
150
Stanford-level course on probabilistic graphical models covering Bayesian networks, Markov random fields, and exact/approximate inference for ML.
What you'll get
Use probability and statistics for ML rigorously
Understand information theory for deep learning
Implement numerical methods for ML algorithms
Fit
Best for
AI Researchers
PhD Students
Mathematicians
Frontier Scientists
Not ideal for
Learners seeking only entry-level overviews
Prerequisites & pricing
Prerequisites
Probability and basic ML
Pricing
Subscription
Certification
Certificate
Growth Leverage: Completing this course positions individuals for roles such as Machine Learning Engineer, Data Scientist, or AI Researcher, where expertise in probabilistic models is crucial. It also opens doors for advanced academic pursuits and certifications related to AI and ML development.
Skills Value: The skills gained from this course address complex problems in AI, leading to high-demand roles with salaries ranging from $100,000 to $150,000. Employers value proficiency in Bayesian networks and inference methods for developing advanced predictive models and improving decision-making processes.
Probabilistic Models
Bayesian Networks
Graphical Models
Inference
Stanford