Uncertainty Quantification Courses and Certifications

Toronto Machine Learning Series (TMLS)

Uncertainty Quantification with Conformal Prediction - A Path to Reliable ML Models

Dive into conformal prediction techniques for quantifying uncertainty in ML models, covering implementation across various domains and practical applications using Python and pre-trained neural networks.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Video

Basin and Petroleum System Modeling

Uncertainty Quantification in Basin and Petroleum System Modeling

Explore uncertainty quantification in basin modeling through Bayesian approaches, seismic data analysis, and rock physics modeling for enhanced geological interpretations.

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

Institute for Pure & Applied Mathematics (IPAM)

Uncertainty Quantification in Ocean-Atmosphere Simulations Using Polynomial Chaos Methods and Gaussian Process Regression

Explore uncertainty quantification techniques in ocean-atmosphere simulations using polynomial chaos methods and Gaussian process regression for parameter calibration.

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

Alan Turing Institute

Deep and Multi-Fidelity Learning with Gaussian Processes

Explore uncertainty quantification using Gaussian Process emulators for complex systems, focusing on deep learning and multi-fidelity approaches in climate, tsunami, and earthquake modeling.

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

Alan Turing Institute

Deep Gaussian Processes for Bayesian Inversion - Matt Dunlop, Courant

Explore deep Gaussian processes for Bayesian inversion, covering theoretical foundations, numerical methods, and applications in uncertainty quantification for complex physical systems.

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

Toronto Machine Learning Series (TMLS)

Quantifying the Uncertainty in Model Predictions Using Conformal Prediction

Learn techniques for quantifying uncertainty in neural network predictions, focusing on conformal prediction as a versatile, statistically rigorous method applicable to classification and regression tasks.

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

Uncertainty in Artificial Intelligence

Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning - Session 2

Explore critical analysis of uncertainty quantification in machine learning, examining conditional entropy and mutual information as measures for aleatoric and epistemic uncertainty.

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

Institute for Pure & Applied Mathematics (IPAM)

Adaptive Computing and Multi-Fidelity Learning - IPAM at UCLA

Explore adaptive computing and multi-fidelity learning for efficient optimization and uncertainty quantification in complex scientific workflows, with applications and scaling challenges discussed.

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

Institute for Pure & Applied Mathematics (IPAM)

Learning Uncertainty-Aware Models of Defect Kinetics at Scale - IPAM at UCLA

Explore advanced techniques for modeling metal properties, including energy landscape exploration, coarse-graining with uncertainty quantification, and descriptor-based approaches for capturing diverse material characteristics.

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

Alan Turing Institute

Efficient and Effective Calibration of Spatio-Temporal Models - Dan Williamson & James Salter, Exeter

Explore efficient calibration techniques for spatio-temporal models, focusing on uncertainty quantification and Gaussian Process emulation in complex physical systems like climate and natural disasters.

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

Alan Turing Institute

High-Dimensional Hierarchical Models for Large-Scale Geophysical Applications

Explore high-dimensional hierarchical models for geophysical applications, focusing on uncertainty quantification and Gaussian Process emulation in complex physical systems.

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

Alan Turing Institute

Sequential Design Based on Mutual Information for Computer Experiments - Joakim Beck, KAUST

Explore uncertainty quantification using Gaussian Process emulators for complex systems. Learn about sequential design methods, mutual information criteria, and applications in climate, tsunami, and earthquake modeling.

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

Alan Turing Institute

Known Boundary Emulation of Complex Computer Models - Ian Vernon, Cambridge

Explore efficient methods for emulating complex computer models using Known Boundary Emulation, focusing on uncertainty quantification and applications in climate, tsunami, and earthquake problems.

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

Alan Turing Institute

Pragmatically Ambitious Multiscale Global Temperature Reconstruction - Finn Lindgren, Edinburgh

Explore pragmatic approaches to global temperature reconstruction using multiscale models, data sources, and statistical techniques for uncertainty quantification and bias correction.

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

Alan Turing Institute

Workshop on Probabilistic Numerical Methods - Panel Discussion

Panel discussion exploring probabilistic numerical methods, covering topics like reference priors, robust uncertainty quantification, and Bayesian inversion methods for improved statistical inference and error quantification.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video