Probabilistic Graphical Models Courses and Certifications

Probabilistic Graphical Models 1: Representation

18 ratings

Encode complex joint distributions as graphs: build Bayesian and Markov networks, template models like HMMs and plate models, structured CPDs, and influence diagrams for decisions.

• Coursera
• 7 weeks, 10 hours a week
• Self-Paced
• Paid Course

Career Certificate

Probabilistic Graphical Models

1552 ratings at Coursera

Encode joint distributions over many interacting random variables: learn graphical representation, inference algorithms, and parameter and structure learning for probabilistic graphical models.

• Coursera
• 16 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

Fundamentals of Probabilistic Graphical Models

Learn to model probability distributions with Bayes Nets and apply Hidden Markov Models to sequence tasks such as spam classification and part-of-speech tagging.

• Udacity
• 14 hours
• Self-Paced
• Paid Course

Probabilistic Graphical Models 2: Inference

491 ratings at Coursera

Answer queries with probabilistic graphical models: variable elimination, clique tree belief propagation, MAP message passing, and sampling methods like Gibbs and Metropolis-Hastings.

• Coursera
• 7 weeks, 5 hours a week
• Self-Paced
• Paid Course

Probabilistic Graphical Models 3: Learning

305 ratings at Coursera

Learn how graphical models are learned from data: maximum likelihood and Bayesian parameter estimation, undirected model training, Bayesian network structure search, and EM for incomplete data.

• Coursera
• 8 weeks, 8 hours a week
• Self-Paced
• Paid Course

Coursera

Probabilistic Graphical Models: A Compact Introduction

Build Bayesian and Markov network representations from conditional independence, run variable elimination and belief propagation, and weigh exact versus sampling inference in Python with pgmpy and pomegranate.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course

Computational and Graphical Models in Probability

Simulate random variables with the inverse transformation and rejection methods, then model relational data using exponential random graph models and probabilistic graphical models in R.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Graphical Models Certification Training

Learn graphical models for decision making: build Bayesian networks and Markov networks, then carry out inference and model learning.

• Edureka
• Self-Paced
• Paid Course

Knowledge-Based AI: Cognitive Systems

2 ratings

The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition.

• Udacity
• 15 hours
• Self-Paced
• Free Online Course

Career Certificate

Artificial Intelligence

235 ratings at Udacity

Explore classical AI methods—search, planning, optimization, adversarial reasoning, and probabilistic graphical models—to build problem-solving agents.

• Udacity
• 40 hours
• Self-Paced
• Paid Course

UofU Data Science

Probabilistic Graphical Models and Maximum Likelihood Estimation - Lecture 13

Dive into probabilistic graphical models, exploring likelihood estimation, Gaussian models, and network structures while learning to analyze real-world data using conditional independence concepts.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Wolfram

The BayesianNetwork Paclet - Graphical Probabilistic Models for Reasoning Under Uncertainty

Explore Bayesian networks for probabilistic modeling and uncertainty reasoning using Wolfram's specialized paclet for multivariate probability distributions.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Career Certificate

Statistical Methods for Computer Science

5 ratings at Coursera

Build advanced probability and statistical modeling skills: joint distributions, expectation, simulation, exponential random graph models, probabilistic graphical models, and hypothesis testing with R.

• Coursera
• 12 weeks, 5 hours a week
• Self-Paced
• Paid Course
• 3 courses

Computational Probability and Inference

3 ratings

Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spa

• edX
• 12 weeks, 4-6 hours a week
• Self-Paced
• Free Certificate

Bayesian Networks 3 - Maximum Likelihood - Stanford CS221: AI (Autumn 2019)

Learn parameter estimation in Bayesian networks using maximum likelihood, Laplace smoothing, and expectation maximization.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Online Course