Probabilistic Graphical Models Courses and Certifications

Fundamentals of Probabilistic Graphical Models

Learn to represent complex probability distributions with Bayes Nets and use Hidden Markov Models for pattern recognition in various fields like computer vision, bioinformatics, and natural language processing.

• Udacity
• 3 weeks, 4-5 hours a week
• On-Demand
• Paid Course

Computational and Graphical Models in Probability

Master computational probability through simulation techniques, network analysis, and graphical models using R. Build expertise in ERGMs and PGMs while gaining practical skills for complex data analysis and machine learning applications.

• Coursera
• 15 hours 56 minutes
• On-Demand
• Free Online Course (Audit)

USENIX

Fast Inference for Probabilistic Graphical Models

Innovative system for efficient probabilistic graphical model inference, offering significant speedup through optimized memory management, parallelization, and flexible integration with sampling algorithms.

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

Uncertainty in Artificial Intelligence

Structure Learning Using Benchpress - UAI 2023 Tutorial

Explore Benchpress, a scalable workflow for structure learning in probabilistic graphical models. Learn to run, develop, and benchmark algorithms for causal discovery and data-generating mechanisms.

• YouTube
• 58 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
• Free Online Course (Audit)

Probabilistic Graphical Models 2: Inference

484 ratings at Coursera

Explore probabilistic inference in graphical models, covering exact and approximate algorithms for conditional probability queries and MAP inference, with hands-on programming assignments.

• Coursera
• 38 hours
• On-Demand
• Free Online Course (Audit)

Microcredential

Probabilistic Graphical Models

Explore probabilistic graphical models, a powerful framework for encoding complex probability distributions, with applications in machine learning, medical diagnosis, and more.

• Coursera
• 17 weeks, 11 hours a week
• On-Demand
• Paid Course
• 3 courses

Probabilistic Graphical Models 3: Learning

299 ratings at Coursera

Learn to build probabilistic graphical models from data, covering parameter estimation, structure learning, and handling incomplete data. Explore advanced techniques for both directed and undirected models.

• Coursera
• 66 hours
• On-Demand
• Free Online Course (Audit)

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

Microcredential

Artificial Intelligence

186 ratings at Udacity

Explore core AI concepts and apply them to real-world problems. Learn search algorithms, planning, optimization, game theory, and probabilistic models. Develop practical skills in Python.

• Udacity
• 2 months 5 hours 36 minutes
• 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
• Free Online Course (Audit)

Mathematical and Computational Foundations for Enabling Predictive Digital Twins at Scale

Explore mathematical foundations for scalable digital twins, focusing on probabilistic graphical models and physics-based reduced-order models in scientific and engineering applications.

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

University of Tübingen

Computer Vision

Explore image formation, structure-from-motion, stereo reconstruction, graphical models, and shape-from-X techniques in this comprehensive introduction to computer vision fundamentals.

• YouTube
• 15 hours
• On-Demand
• Free Online Course

Aladdin Persson

Latent Dirichlet Allocation with Gibbs Sampling Explained

Comprehensive explanation of Latent Dirichlet Allocation (LDA) with Gibbs Sampling, covering topic modeling, posterior inference, and implementation details. Ideal for those interested in understanding this classic machine learning technique.

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

Simons Institute

Mind Over Data - The One Thing You Know and Machines Don't

Explore the intersection of human intuition and machine learning in causal reasoning, examining Bayesian networks, confounding factors, and real-world applications in AI and data analysis.

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