Gaussian Processes Courses and Certifications

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

Uncertainty in Artificial Intelligence

Geometric Probabilistic Models - Tutorial 4

Explore geometric probabilistic models for data-efficient, uncertainty-aware decision-making in applications like drug design and robotics. Learn theory and implementation of Gaussian processes and related techniques.

• YouTube
• 2 hours 32 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

How to Distinguish Fractional Brownian Motion with Random and Constant Hurst Exponents

Explore fractional Brownian motion models, distinguishing between constant and random Hurst exponents using statistical methods. Apply to financial markets and biological systems.

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

INI Seminar Room 2

Exploiting Anisotropy in Gaussian Process Emulation

Explore advanced techniques in Gaussian process emulation, focusing on anisotropic properties and their applications in statistical modeling and data-driven engineering.

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

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Information Geometric and Optimal Transport Framework for Gaussian Processes

Delve into advanced mathematical concepts combining Information Geometry and Optimal Transport with Gaussian processes, exploring theoretical properties and practical applications in machine learning.

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

INI Seminar Room 2

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction and Optimization

Explore advanced meta-learning techniques for molecular property prediction using deep kernel Gaussian processes, focusing on optimization strategies and statistical foundations for data-driven engineering.

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

Galileo Galilei Institute (GGI)

Trained Quantum Neural Networks and Gaussian Processes

Explore quantum neural networks and Gaussian processes through advanced training methodologies in this specialized quantum machine learning presentation.

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

Squid: Schools for Quantum Information Development

Trained Quantum Neural Networks are Gaussian Processes

Explore the mathematical foundations of quantum neural networks, focusing on their convergence to Gaussian processes and implications for training in supervised learning scenarios.

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

Introduction to Uncertainty Quantification

10 ratings at Coursera

Master the fundamentals of Uncertainty Quantification through probability theory, uncertainty propagation, surrogate modeling, reliability analysis, and Bayesian methods for mathematical and engineering systems.

• Coursera
• 1 day 5 hours 57 minutes
• On-Demand
• Paid Course

Introduction to Scientific Machine Learning

1 review

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)

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

Richard McElreath

Statistical Rethinking 2022

Master Bayesian inference, multilevel modeling, and causal reasoning through practical statistical methods that avoid common pitfalls in data analysis and research design.

• YouTube
• 1 day 46 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Learning-Based Model Predictive Control - Towards Safe Learning in Control

Explores integrating learning techniques with optimization-based control for safe, high-performance systems. Discusses methods for data-driven modeling, safety filters, and constraint satisfaction in robotics applications.

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

Abhishek Thakur

Hyperparameter Optimization - This Tutorial Is All You Need

Explore various hyperparameter optimization techniques including Grid Search, Random Search, Bayesian Optimization, Hyperopt, and Optuna to fine-tune models and optimize functions effectively.

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