Introduction to the Course

Overview

Generative artificial intelligence (AI) refers to systems that can generate new content, such as images, text, or even entire datasets, similar to what it has been trained on. These systems are based on generative models, which learn the underlying patterns and structures of the training data and use that knowledge to create new, similar data.

Diffusion models are probabilistic generative models used in machine learning. They are primarily employed for modeling and generating sequential data like text, time series, and images. Diffusion models are designed to capture the complex dependencies and patterns within data, making them useful for image generation, text generation, and denoising tasks. In this course, we’ll focus on image generation using such models.

Why take this course?

This course provides insights into the applications of diffusion models, equipping learners with the skills needed for data analysis, prediction, and decision-making processes. The mathematical and computational aspects involved in diffusion models contribute to developing problem-solving skills, making knowledge applicable to interdisciplinary research and professional development. Whether aiming for research contributions, career advancement, or cross-disciplinary expertise, the insights gained from a diffusion models course can be highly beneficial in navigating diverse fields and industries.

Target audience

The target audience includes individuals from various academic and professional backgrounds. This course is suitable for students, researchers, and professionals interested in gaining a deep understanding of complex processes and their applications across different disciplines. Specifically, those in fields such as data science, machine learning, computer vision, and deep learning may find the course particularly relevant.

Prerequisite

To get the most out of this course, learners should be familiar with basic machine learning and Python concepts.