Probabilistic Models Courses and Certifications

Uncertainty in Artificial Intelligence

Probabilistic Flow Circuits: Towards Deep Models for Tractable Inference - Oral Session 6

Explore a novel approach combining probabilistic circuits with normalizing flows for enhanced expressivity while maintaining tractable inference in deep probabilistic models.

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

Mathematical Foundations of Generative AI

Explore the mathematical foundations of generative AI, covering VAEs, GANs, diffusion models, and LLMs with hands-on PyTorch implementations for both theoretical and practical mastery.

• Swayam
• Self-Paced
• Free Online Course

USENIX

Differentially Private Diffusion Probabilistic Models for Image Synthesis

Explore how dp-promise leverages diffusion model noise to achieve differential privacy in image synthesis while maintaining high image quality and protecting sensitive training data.

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

Stanford Seminar - Stories from CoCoLab: Probabilistic Programs, Cognitive Modeling, & Smart Web Pages

Explore probabilistic programs, cognitive modeling, and smart web pages through CoCoLab's research. Gain insights into thinking processes, language understanding, and innovative experiment designs.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Online Course

Stanford CS236 - Deep Generative Models - Lecture 1: Introduction

Explore deep generative models with Stanford's Prof. Ermon. Learn key concepts, applications, and cutting-edge techniques in this comprehensive introduction to a rapidly evolving field of AI.

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

NPTEL;  IIT Kharagpur

Probability Methods in Civil Engineering

Comprehensive exploration of probability concepts, distributions, and statistical methods applied to civil engineering problems, enhancing analytical skills for real-world applications.

• YouTube
• 1 day 13 hours
• Self-Paced
• Free Video

Yannic Kilcher

Diffusion Models Beat GANs on Image Synthesis - Machine Learning Research Paper Explained

Explore how Diffusion Models outperform GANs in image synthesis, with improved architecture, classifier guidance, and better sample quality while maintaining distribution coverage.

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

Deep Generative Models - Discrete Latent Variable Models - Lecture 17

Explore discrete latent variable models in deep generative modeling, covering key concepts and applications in AI and machine learning.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Stanford CS236: Deep Generative Models - Lecture 13 - Score Based Models

Explore score-based models in deep generative modeling, covering theoretical foundations and practical applications in this advanced Stanford lecture on AI and machine learning.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

Deep Generative Models - Lecture 5: Variational Autoencoders

Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

Deep Generative Models - Lecture 6: Variational Autoencoders

Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this Stanford lecture by Prof. Stefano Ermon.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

Towards Safe and Efficient Learning in the Physical World - Stanford Seminar

Exploring safe Bayesian optimization and model-based deep reinforcement learning for efficient, safe online learning in real-world applications like robotics and laser tuning.

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

Simons Institute

Nonparametric Bayesian Methods - Models, Algorithms, and Applications IV

Explore probabilistic models for graphs, edge exchangeability, and graph paintbox representations in this advanced machine learning lecture on nonparametric Bayesian methods.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Joint Embedding Method and Latent Variable Energy Based Models

Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.

• YouTube
• 1 hour 52 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Energy Based Models and Self-Supervised Learning

Explore energy-based models, latent variables, and self-supervised learning. Dive into EBM training, inference, and comparison with probabilistic models. Discover contrastive methods and applications like K-means and BERT.

• YouTube
• 1 hour 37 minutes
• Self-Paced
• Free Video