Gaussian Processes Courses and Certifications

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

media.ccc.de

A Few Quantitative Thoughts on Parking in Marburg - RC3 - 2020

Analyse des données de stationnement à Marburg : tendances, modèles et prévisions pour optimiser l'utilisation des espaces de stationnement et améliorer la planification urbaine.

• YouTube
• 28 minutes
• On-Demand
• Conference Talk

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

Open Data Science

Making Robust Business Decisions with Sparse Data - Sarah Jarvis, PhD | ODSC Europe 2019

Explore data-efficient solutions for supply chain management, focusing on forecasting with limited data and making robust decisions in complex, dynamic systems.

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

Alan Turing Institute

The Automatic Statistician - Professor Zoubin Ghahramani

Explore Bayesian model selection strategies for automated data analysis and report generation, with insights on regression models, Gaussian processes, and computational resource allocation.

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

Institute for Pure & Applied Mathematics (IPAM)

Assisting 4D-STEM Data Processing by Machine Learning and Bayesian Optimization

Explore machine learning and Bayesian optimization techniques for processing 4D-STEM data, enhancing efficiency in materials analysis and electron ptychography reconstruction.

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

Institute for Pure & Applied Mathematics (IPAM)

Methods for Scalable Probabilistic Inference - IPAM at UCLA

Explore scalable probabilistic inference methods for astrophysics, focusing on Gaussian Processes for time series analysis and open-source tools to accelerate Bayesian inference with large datasets.

• YouTube
• 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

Institute for Pure & Applied Mathematics (IPAM)

Uncertainty Quantification of Quantum Chemical Methods - IPAM at UCLA

Explore uncertainty quantification in quantum chemistry, covering error estimation, benchmarking, and machine learning approaches for improved accuracy in computational methods.

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

Applied Algebraic Topology Network

Takashi Owada - Limit Theorems for Topological Invariants of the Dynamic Multi-Parameter Simplicial Complex

Explore topological invariants in dynamic multi-parameter simplicial complexes, covering connectivity, probability, and limiting processes in applied algebraic topology.

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

International Mathematical Union

Convergence of a Dilute Gas to the Fluctuating Boltzmann Equation

Explore the convergence of a dilute gas to the fluctuating Boltzmann equation, examining empirical measure fluctuations and their convergence to a Gaussian process for short and long time periods.

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

MLCon | Machine Learning Conference

Deep Probabilistic Modelling with Pyro

Explore deep probabilistic modeling with Pyro, combining probabilistic methods and neural networks for better uncertainty handling and decision-making in machine learning applications.

• YouTube
• 33 minutes
• On-Demand
• Conference Talk

Institute for Pure & Applied Mathematics (IPAM)

Solving Overparametrized Systems of Nonlinear Equations

Explore overparametrized nonlinear equation systems, their solutions, and implications for neural network optimization landscapes with Stanford's Andrea Montanari at IPAM's EnCORE Workshop.

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

Simons Institute

Data is as Data Does - The Influence of Computation on Inference

Exploring the impact of computational approximations on probabilistic models in machine learning, with focus on Gaussian Process approximations and their applications in neurobiology.

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

Simons Institute

Explorations in Exploration: Deep Learning Meets Value of Information for Sequential Experimental Design

Exploring machine learning's role in sequential experimental design, focusing on quantifying information value and improving flexibility in planning future experiments.

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