Bayesian Networks Courses and Certifications

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
• On-Demand
• 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
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Bayesian Networks for Causal Reasoning by Tavpritesh Sethi

Explore advanced Bayesian network techniques for causal reasoning in healthcare, focusing on practical applications and bridging the gap between mathematical modeling and clinical problem-solving.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Bayesian Networks for Causal Reasoning - Lecture 1

Introduction to Bayesian networks for causal reasoning in healthcare, exploring their application in analyzing clinical data and inferring patterns to improve patient outcomes and medical decision-making.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Bayesian Networks for Causal Reasoning by Tavpritesh Sethi

Explore advanced Bayesian network techniques for causal reasoning in healthcare, focusing on practical applications and interpretation of complex medical data for improved decision-making.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Bayesian Networks for Causal Reasoning II by Tavpritesh Sethi

Explore advanced Bayesian network techniques for causal reasoning in healthcare, focusing on practical applications and interpretation of complex medical data for improved decision-making.

• YouTube
• 1 hour 42 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Higher Order Bayesian Networks, Exactly - POPL 2024

Explore a higher-order programming language for Bayesian networks, combining advanced techniques from linear logic, intersection types, and rewriting theory for efficient probabilistic modeling and inference.

• YouTube
• 16 minutes
• On-Demand
• Free Video

International Centre for Theoretical Sciences

Some Problems Regarding Bayesian Networks

Exploration of challenges in Bayesian networks, discussing structural properties, dynamics, and applications in probability theory and network science.

• YouTube
• 59 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Bayesian Networks - Algorithms and Structures for ASR

Explore Bayesian networks algorithms and structures for automatic speech recognition with IBM researcher Geoffrey Zweig in this technical seminar.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Recent Innovations in Dynamic Bayesian Networks for Automatic Speech Recognition

Explore cutting-edge Dynamic Bayesian Network innovations transforming automatic speech recognition systems and their practical applications.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Probabilistic Graphical Models 1: Representation

18 reviews

Explore probabilistic graphical models, including Bayesian networks and Markov networks, to encode complex probability distributions for applications like medical diagnosis and speech recognition.

• Coursera
• 67 hours
• On-Demand
• Paid Course

Time, Change, and Decisions for Marketing

Revolutionize marketing strategies with advanced analytics, optimizing decision-making through Bayesian networks, time series forecasting, and causal modeling for data-driven, adaptive campaigns.

• Coursera
• 22 hours 53 minutes
• On-Demand
• Paid Course

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

Explore maximum likelihood estimation in Bayesian networks, covering parameter learning, probabilistic inference, and advanced concepts like regularization and Expectation Maximization.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Online Course

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

Explore advanced probabilistic inference techniques, including hidden Markov models, particle filtering, and Gibbs sampling, for AI applications in object tracking and more.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Online Course

Bayesian Networks 1 - Inference - Stanford CS221: AI

Explore Bayesian networks for AI, covering probabilistic inference, modeling, and applications in object tracking, language modeling, and document classification.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Online Course