Bayesian Deep Learning Courses and Certifications

Institute for Pure & Applied Mathematics (IPAM)

Scalable Algorithms for Bayesian Deep Learning via Stochastic Gradient Monte Carlo and Beyond

Discover advanced Monte Carlo methods for Bayesian deep learning, focusing on replica exchange SGMCMC algorithms that overcome scalability challenges in big data neural network training.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Uncertainty in Artificial Intelligence

An Improved Variational Approximate Posterior for the Deep Wishart Process - Oral Session 1

Explore an improved variational approximate posterior for the Deep Wishart Process, enhancing predictive performance through generalized Bartlett decomposition with minimal computational cost increase.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Finnish Center for Artificial Intelligence FCAI

Functional Priors for Bayesian Deep Learning

Explore functional priors for Bayesian deep learning, focusing on imposing Gaussian process priors on neural networks through Wasserstein distance minimization.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Bayesian Machine Learning in Python: A/B Testing

7963 ratings at Udemy

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

• Udemy
• 10 hours 27 minutes
• Self-Paced
• Paid Course

Institute for Advanced Study

PAC-Bayesian Approaches to Understanding Generalization in Deep Learning - Gintare Dziugaite

Explore PAC-Bayesian approaches for understanding generalization in deep learning, covering risk bounds, optimal priors, and data-dependent techniques for neural networks.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

USENIX

Bayesian Code Diffusion for Efficient Automatic Deep Learning Program Optimization

Discover how Bayesian code diffusion accelerates deep learning compiler auto-tuning by efficiently searching optimal program code through iterative diffusion, achieving up to 3.31× speedup.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Deep Learning New Frontiers

Explore deep learning limitations and new frontiers, including expressivity, generalization, adversarial attacks, uncertainty, and AutoML. Gain insights into cutting-edge developments in neural networks.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

MIT 6.S191 - Deep Learning Limitations and New Frontiers

Explore deep learning limitations, adversarial attacks, uncertainty quantification, and AutoML in this lecture. Gain insights into neural network challenges and emerging frontiers in AI research.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Deep Learning Limitations and New Frontiers

Explore deep learning limitations, new frontiers, and advancements in AI, including uncertainty estimation, adversarial attacks, and meta-learning, with insights on future developments and applications.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Making Machines That See - Geometry, Uncertainty and Deep Learning

Explore computer vision advancements, from 3D object reconstruction to real-time deep learning systems incorporating geometry and uncertainty, with applications in robotics and mobile technology.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Poisson Random Fields for Dynamic Feature Models

Explore dynamic feature allocation models using Poisson random fields. Learn about the Wright-Fisher Indian Buffet Process and its application to topic modeling for time-stamped documents, with a focus on NIPS papers.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Learning Steering for Parallel Autonomy

Explores end-to-end learning for parallel autonomy in self-driving cars, focusing on handling ambiguity, uncertainty estimation, and decision-making capabilities beyond reactionary control.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Data Science Festival

Bayesian Machine Learning - Benefits, Drawbacks, and Practical Applications

Explore Bayesian machine learning's benefits and drawbacks, focusing on model uncertainty and prior knowledge injection. Learn practical applications from Wise's Head of AI.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Uncertainty in Artificial Intelligence

Deep Learning Advances in Uncertainty and Generative Modeling - Session 1

Explore cutting-edge deep learning research, including Bayesian methods, generative modeling, visual concept programming, conformal regression, and heteroskedastic regression analysis.

• YouTube
• 2 hours 3 minutes
• Self-Paced
• Free Video

The Julia Programming Language

What's New in Trustworthy AI in Julia (Taija) - JuliaCon 2024

Discover the latest developments in Julia's Trustworthy AI ecosystem, exploring advancements in model explainability, algorithmic recourse, uncertainty quantification, and Bayesian deep learning through the Taija framework.

• YouTube
• 30 minutes
• Self-Paced
• Free Video