DATA 446 - Generative AI

Credits: (3)
Prerequisite(s): DATA 301
This course offers an in-depth exploration of Generative Artificial Intelligence (AI), a branch of AI focused on creating models that can generate new content, such as images, text, and sounds, mimicking human-like creativity. The curriculum is designed to explore the foundational theories and practical applications of generative models, including but not limited to Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
Throughout the course, students will:

Understand the Core Principles of Generative AI: Grasp the theoretical underpinnings and algorithms that enable machines to generate novel, realistic content.
Explore Different Generative Models: Learn about various generative architectures, including GANs, VAEs, and transformers, understanding their unique characteristics and applications.
Apply Generative AI in Practice: Engage in hands-on projects that involve training, tuning, and deploying generative models to produce creative content across different domains, such as art, music, and natural language generation.

Programming assignments and projects will be carried out in Python, leveraging popular machine learning libraries like TensorFlow and PyTorch. By the end of this course, students will be proficient in designing and implementing generative AI models, capable of producing innovative and complex outputs that reflect aspects of human creativity.