Generative and Foundation Models

The overall goal of this course is to provide students with the foundation needed to understand, analyze, and develop generative and foundation models for modern AI applications.

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Explore the transformative world of Generative AI and Foundation Models! This course introduces the principles, architectures, and practical applications behind large-scale AI systems such as large language models, diffusion models, and multimodal generative systems. Students will learn the mathematical and computational foundations of modern generative modeling while gaining hands-on experience building and fine-tuning models for text, image, and multimodal generation tasks. Through practical projects and critical discussions, participants will explore the opportunities, limitations, and ethical implications of contemporary AI systems.

By the end of the course, students will have acquired:

1. An understanding of the theoretical foundations of generative modeling, including probabilistic modeling, transformers, attention mechanisms, diffusion models, and representation learning.
2. Hands-on experience in training, fine-tuning, evaluating, and deploying foundation models using modern machine learning frameworks and tools.
3. The ability to analyze, discuss, and critically evaluate the design, capabilities, limitations, and societal impacts of generative AI systems across a variety of real-world applications.

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