APPLIED GENERATIVE AI

This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.

Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.

By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.