Foundations of Generative AI

This course provides a comprehensive introduction to generative AI, combining theoretical foundations with hands-on practice. Students will explore the historical evolution of generative models, beginning with early statistical methods and advancing through recurrent and convolutional networks to modern transformers and diffusion-based systems. Emphasis is placed on understanding how these models work, why they matter, and how they are applied in real-world scenarios. The course also equips students with practical skills in transformer architectures, prompt engineering, instruction tuning, and parameter-efficient fine-tuning. Finally, students will engage with the ethical and responsible use of generative AI, learning frameworks for governance, fairness, and safety. By the end of the course, students will be able to build, tune, and critically evaluate generative AI systems, while also articulating the societal and organizational implications of their use.