Bayesian Networks Courses and Certifications

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

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Learn exact and approximate inference in hidden Markov models using forward-backward, particle filtering, and Gibbs sampling.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Online Course

Bayesian Networks 1 - Inference - Stanford CS221: AI

Learn how Bayesian networks compactly represent joint distributions and support probabilistic inference, including explaining away, evidence conditioning, and applications in tracking and classification.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Online Course

UCLA Automated Reasoning Group

Learning and Reasoning with Bayesian Networks

Master probabilistic reasoning and machine learning through comprehensive coverage of Bayesian networks, from basic probability theory to advanced inference algorithms and causal modeling.

• YouTube
• 22 hours 11 minutes
• Self-Paced
• Free Video

UCLA Automated Reasoning Group

Bayesian Networks - Selected Lectures

Discover probabilistic reasoning fundamentals through Bayesian networks, covering syntax, semantics, inference methods, parameter learning, and advanced causality concepts.

• YouTube
• 7 hours 32 minutes
• Self-Paced
• Free Video

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

UofU Data Science

Probabilistic Machine Learning - Lecture 21

Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Machine Learning - Lecture 18

Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical frameworks and practical applications in modern computational analysis.

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

UofU Data Science

Probabilistic Machine Learning - Lecture 16

Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical and practical aspects of modern data analysis and machine learning techniques.

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

UofU Data Science

Probabilistic Machine Learning - Lecture 15

Dive into advanced data science concepts through a comprehensive graduate-level lecture covering key theoretical foundations and practical applications in modern data analysis and machine learning.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Machine Learning - Lecture 14

Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

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

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

Conf42

Cloud-Native AI for Fraud Prevention with Bayesian Networks

Discover how to implement cloud-native AI solutions using Bayesian networks for real-time fraud detection, covering dynamic risk scoring, ML processing, and model drift management.

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