Probabilistic Inference Courses and Certifications

University of Lausanne

Challenging Forensic Science: How Science Should Speak to Court

531 ratings at Coursera

Explore forensic science's role in court, challenging common misconceptions and promoting critical thinking. Learn about DNA analysis, evidence interpretation, and potential pitfalls in forensic practices.

• Coursera
• 16 hours 51 minutes
• Self-Paced
• Paid Course

Reasoning Under Uncertainty

Explore foundational AI concepts including probability, Bayesian Networks, Hidden Markov Models, and Markov Decision Processes to master reasoning and decision-making under uncertainty.

• Coursera
• 12 hours
• Self-Paced
• Paid Course

EDUCBA

Understand and Apply Artificial Intelligence Fundamentals

18 ratings at Coursera

Explore AI fundamentals from core concepts to machine learning and reinforcement learning techniques for intelligent decision-making systems.

• Coursera
• 5 hours 58 minutes
• Self-Paced
• Paid Course

Simons Institute

Scalable and Reliable Inference for Probabilistic Modeling

Explore scalable and reliable inference techniques for probabilistic modeling, focusing on Metropolis-Hastings, Gibbs sampling, and stochastic gradient MCMC methods for efficient data analysis and deep learning applications.

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

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
• Self-Paced
• 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
• Self-Paced
• Free Online Course

Fields Institute

Probabilistic Numerics for Inference with Simulations

Explore probabilistic numerics for inference with simulations, covering high-dimensional models, numerical methods, and innovative approaches to machine learning challenges.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

ACM SIGPLAN

Probabilistic Programming with Programmable Variational Inference

Explore a modular approach to variational inference in probabilistic programming, enhancing expressiveness and enabling user-defined objectives with systematic gradient estimation transformations.

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

ACM SIGPLAN

Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures

Explore a novel approach to probabilistic program inference using Gaussian mixtures, offering improved accuracy and efficiency for complex models and distributions.

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

ACM SIGPLAN

Bayesian Inference Using Probabilistic Programming

Explore Bayesian inference techniques and probabilistic programming concepts with expert insights from Hong Ge at LAFI'24.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

UofU Data Science

Probabilistic Graphical Models and Mean Field Inference - Lecture 20

Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.

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

WestGrid

Bayesian Inference in JAX - Introduction to Probabilistic Programming

Master probabilistic modeling and Bayesian inference techniques using JAX framework, exploring efficient implementation methods for statistical computations and machine learning applications.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

ACM SIGPLAN

Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving

Explore a novel approach for deriving guaranteed bounds on normalized posterior distributions in probabilistic programs using polynomial solving techniques.

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

VinAI

Learning and Amortized Inference in Probabilistic Programs

Explore probabilistic programming, amortized inference, and novel learning methods like reweighted wake-sleep and thermodynamic variational objective for efficient Bayesian inference in complex models.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

Uncertainty in Artificial Intelligence

Probabilistic Flow Circuits: Towards Deep Models for Tractable Inference - Oral Session 6

Explore a novel approach combining probabilistic circuits with normalizing flows for enhanced expressivity while maintaining tractable inference in deep probabilistic models.

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