Probabilistic Programming Courses and Certifications

ACM SIGPLAN

Measurable Cones and Stable Measurable Functions - A Model for Probabilistic Higher-Order Programming

Explore the mathematical foundations of probabilistic functional programming through measurable cones, stable functions, and denotational semantics for extending PCF with probabilistic features.

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

Computational Genomics Summer Institute CGSI

Using the Pyro Probabilistic Programming Language for Statistical Genomics

Discover how Pyro probabilistic programming language applies to statistical genomics through practical implementation and real-world research examples.

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

Center for Language & Speech Processing(CLSP), JHU

Turning Probabilistic Reasoning into Programming

Discover how to transform probabilistic reasoning concepts into practical programming techniques through expert insights from Harvard University's Avi Pfeffer.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

ACM SIGPLAN

Data-oriented Design for Differentiable, Probabilistic Programming

Explore data-oriented design techniques for reimplementing LazyPPL's lazy rose tree structure, focusing on memory optimization and specialized reverse-mode autodiff for probabilistic programming.

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

ACM SIGPLAN

Sandwood: Runtime Adaptable Probabilistic Programming for Java

Discover Sandwood, an open-source probabilistic programming language for Java that enables easier integration of Bayesian models with runtime-adaptable configurations targeting different hardware and execution models.

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

ACM SIGPLAN

Multi-Language Probabilistic Programming

Discover MultiPPL, a framework enabling seamless interoperability between different probabilistic programming languages for complex heterogeneous inference tasks.

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

ACM SIGPLAN

A Domain-Specific Probabilistic Programming Language for Reasoning about Reasoning - Or: A Memo on memo

Discover memo, a specialized probabilistic programming language for modeling theory of mind with faster performance and simplified syntax for recursive reasoning tasks.

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

Alan Turing Institute

Introduction to Machine Learning - Professor Zoubin Ghahramani, Cambridge University

Comprehensive overview of machine learning fundamentals by renowned expert, covering statistical approaches, Bayesian methods, deep learning, and automated data analysis techniques.

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

Institute for Advanced Study

Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum

Explore how machines can achieve more human-like learning through probabilistic programming, intuitive psychology, and simulation engines. Discover insights on core knowledge and one-shot learning in AI.

• YouTube
• 1 hour 14 minutes
• On-Demand
• Free Video

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
• On-Demand
• Free Video

Alan Turing Institute

Semantic Models for Higher-Order Bayesian Inference - Sam Staton, University of Oxford

Explore probabilistic programming for Bayesian modeling, focusing on advanced languages with higher-order functions and continuous distributions. Learn about quasi-Borel spaces and modular inference algorithms.

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

ACM SIGPLAN

Guaranteed Bounds for Discrete Probabilistic Programs with Loops via Generating Functions - LAFI'24

Explore a technique for finding guaranteed bounds on posterior distributions in probabilistic programs with loops using probability generating functions and inductive invariants.

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

ACM SIGPLAN

Lower Bounds for Possibly Divergent Probabilistic Programs

Explore a new proof rule for verifying lower bounds on quantities of probabilistic programs, applicable to possibly divergent programs and non-trivial cases like three-dimensional random walks.

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

ACM SIGPLAN

A Gradual Probabilistic Lambda Calculus

Explore a gradual probabilistic lambda calculus combining static and dynamic typing for probabilistic programming, enhancing flexibility in machine learning and differential privacy applications.

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