Bayesian Inference Courses and Certifications

Bayesian Modeling with RJAGS

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Introduction to Scientific Machine Learning

Learn the basics of machine learning with hands-on practical examples on engineering applications.

• edX
• 16 weeks, 6-7 hours a week
• On-Demand
• Free Online Course (Audit)

Applied Bayesian for Analytics

Learn how to construct, fit, estimate and compute Bayesian statistical models with the help of OpenBUGS (freely available software)

• edX
• 6 weeks, 2-3 hours a week
• On-Demand
• Free Online Course (Audit)

IIT Bombay;  NPTEL

Dealing with materials data : collection, analysis and interpretation

Learn to collect, analyze, and interpret materials data using R programming, covering probability, statistics, data presentation, and experimental design for materials science and engineering applications.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

The Julia Programming Language

Celeste.jl - Petascale Computing in Julia

Explore Celeste.jl, a Julia-based project for petascale astronomical image analysis using Bayesian inference, showcasing Julia's power in high-performance computing and data science on supercomputers.

• YouTube
• 52 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

Simons Institute

Statistical Inference I

Explore statistical inference fundamentals, including models, estimation, minimax risk, and Bayesian approaches, with UC Berkeley's Will Fithian.

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

Simons Institute

Computation in Very Wide Neural Networks

Explore infinite-width neural networks, their correspondence to Gaussian processes, and implications for deep learning performance and dynamics in parameter space.

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

Simons Institute

Dreamcoder- Bootstrapping Inductive Program Synthesis With Wake-Sleep Library Learning

Explore program induction and library learning in AI, focusing on DreamCoder's approach to synthesizing models and systems through neural-guided Bayesian inference and abstraction refinement.

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

Open Data Science

The Hamiltonian Monte Carlo Revolution Is Open Source - Probabilistic Programming with PyMC3

Explore Bayesian statistical computation and Hamiltonian Monte Carlo algorithms using PyMC3, an open-source probabilistic programming package. Learn to apply these advanced techniques to complex models in Python.

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

Alan Turing Institute

Statistics in Cyber-Security - Dr Nick Heard, Imperial College London

Explore statistical approaches for cyber-security, from micro-level models to full network graph representations, to identify illegitimate network activity using enterprise data sources.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Alan Turing Institute

Formal Verification and Learning of Complex Systems - Professor Alessandro Abate

Exploring formal verification techniques for complex systems, combining model-based and data-driven methods to address scalability issues and provide system-level assertions in Cyber-Physical Systems.

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

Richard McElreath

Statistical Rethinking 2023 - Markov Chain Monte Carlo

Explore Markov Chain Monte Carlo methods, from basic concepts to advanced techniques like Hamiltonian Monte Carlo, with practical applications and diagnostics for statistical modeling.

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

Richard McElreath

Science Before Statistics - Causal Inference

Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.

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

MITCBMM

Learning What We Know and Knowing What We Learn - Gaussian Process Priors for Neural Data Analysis

Explore Gaussian process priors for neural data analysis, covering latent variable models, Bayesian inference, covariance kernels, and factor analysis. Gain insights into advanced techniques for understanding complex neural datasets.

• YouTube
• 1 hour 31 minutes
• On-Demand
• Free Video