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.
