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
• 14 hours
• On-Demand
• Paid Course

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

Coursera

Probabilistic Graphical Models: A Compact Introduction

Master probabilistic graphical models for medical diagnosis and risk prediction with Bayesian networks, inference algorithms, and Python implementation using pgmpy.

• Coursera
• 2 hours 40 minutes
• On-Demand
• Paid Course

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

Microcredential

Probabilistic Graphical Models

1549 ratings at Coursera

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

• Coursera
• 16 weeks, 10 hours a week
• On-Demand
• Paid Course
• 3 courses

Probabilistic Graphical Models 2: Inference

489 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
• 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
• Paid Course

Probabilistic Graphical Models 3: Learning

304 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
• Paid Course

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

Microcredential

Artificial Intelligence

204 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
• 40 hours
• On-Demand
• Paid Course

Neuro Symbolic

Markov Random Fields, Markov Chains, and Markov Logic Networks - Probabilistic Graphical Models Tutorial

1 review

Explore Markov Random Fields, Logic Networks, and Chains. Dive into probabilistic graphical models with Professor Simari, covering key concepts and applications in AI and machine learning.

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

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 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