🟦 Diffusion Models & Deep Generative Learning Course

This advanced course explores diffusion models and modern deep generative learning techniques used in artificial intelligence research and real-world AI applications. Designed for learners with an interest in machine learning and deep learning, the course provides a detailed understanding of probabilistic generative models and image generation technologies.

Students will begin with foundational concepts and mathematical background required for understanding diffusion-based AI systems. The course introduces score-based generative modeling, Langevin dynamics, and the principles behind denoising diffusion probabilistic models (DDPM). Learners will gain insight into how diffusion models generate high-quality images and synthetic data through iterative denoising processes.

The course also covers acceleration techniques used to improve model efficiency and reduce computational costs in diffusion systems. Advanced topics include guided diffusion methods, which allow greater control over AI-generated outputs, and inverse problems, where diffusion models are applied to image restoration, reconstruction, and enhancement tasks.

Through in-depth lectures and technical discussions, learners will explore state-of-the-art AI research concepts widely used in modern generative AI systems. By the end of the course, students will have a strong understanding of diffusion-based deep learning models and their applications in computer vision, generative AI, and scientific machine learning.

🟨 1. Foundations of Diffusion Models

This section introduces the mathematical and conceptual foundations behind diffusion-based generative models.

🟩 1.1 Probability and Generative Modeling Basics

Understand the probabilistic concepts behind generative AI models and data distribution learning.

🟩 1.2 Score-Based Generative Models

Learn how score functions are used to model and generate complex data distributions.

🟩 1.3 Langevin Dynamics

Explore stochastic processes used to gradually sample from complex probability distributions.

🟨 2. Denoising Diffusion Probabilistic Models (DDPM)

This section explains how diffusion models generate data through iterative denoising processes.

🟩 2.1 Forward Diffusion Process

Learn how data is gradually corrupted with noise over multiple steps.

🟩 2.2 Reverse Diffusion Process

Understand how models reconstruct data by removing noise step-by-step.

🟩 2.3 Training Diffusion Models

Explore how neural networks are trained to learn denoising functions.

🟨 3. Acceleration Techniques in Diffusion Models

This section focuses on improving efficiency and reducing computational cost in diffusion systems.

🟩 3.1 Sampling Optimization Methods

Learn techniques to speed up image generation without losing quality.

🟩 3.2 Model Efficiency Improvements

Understand architectural and algorithmic improvements for faster inference.

🟨 4. Guided Diffusion Methods

This section explores how diffusion models can be controlled for better outputs.

🟩 4.1 Classifier Guidance

Learn how external classifiers guide image generation.

🟩 4.2 Classifier-Free Guidance

Understand modern guidance techniques without external classifiers.

🟩 4.3 Controllable Generation Techniques

Explore methods to control style, structure, and content of generated outputs.

🟨 5. Inverse Problems in Diffusion Models

This section applies diffusion models to reconstruction and restoration tasks.

🟩 5.1 Image Restoration

Use diffusion models to restore corrupted or low-quality images.

🟩 5.2 Image Reconstruction

Learn how missing or incomplete data can be reconstructed.

🟩 5.3 Image Enhancement

Improve image quality using diffusion-based techniques.

🟨 6. Applications of Diffusion Models

This section highlights real-world applications of diffusion models in AI systems.

🟩 6.1 Computer Vision Applications

Explore how diffusion models are used in image synthesis and editing.

🟩 6.2 Generative AI Systems

Understand how diffusion powers modern generative AI tools.

🟩 6.3 Scientific Machine Learning

Learn how diffusion models are applied in scientific research and simulations.

🟨 7. Final Learning Outcomes

By the end of this course, learners will have a strong theoretical and practical understanding of diffusion models, enabling them to apply these techniques in AI research, computer vision, and generative AI applications.